WO2025165884A1

WO2025165884A1 - Bioelectric isolation and localization using time difference of arrival (tdoa)

Info

Publication number: WO2025165884A1
Application number: PCT/US2025/013602
Authority: WO
Inventors: Lee M. Miller
Original assignee: University of California Berkeley; University of California San Diego UCSD
Current assignee: University of California Berkeley; University of California San Diego UCSD
Priority date: 2024-01-30
Filing date: 2025-01-29
Publication date: 2025-08-07
Anticipated expiration: 2026-07-30

Abstract

Systems and methods for localization of electrical activity in a body include receiving, by a computing device via a first sensor disposed on or in a body, a first time value associated with electrical activity in the body and receiving, by the computing device via a second sensor disposed on or in the body, a second time value associated with the electrical activity in the body. The computing device computes, based on the first time value and the second time value, a time difference of arrival between the first sensor and the second sensor. Based on the computed time difference of arrival, the computing device identifies a location from which the electrical activity in the body originated. The computing device performs an action based on the identified location.

Description

Bioelectric Isolation and Localization Using Time Difference of Arrival (TDOA)

CROSS-REFERENCES TO RELATED APPLICATIONS

[0001] This application claims the benefit of and the priority to U.S. Provisional Application No. 63/626,970, filed January 30, 2024, entitled “Bioelectric Isolation And Localization Using Time-Difference-Of-Arrival (TDOA), which is incorporated by reference herein in its entirety.

STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT

[0002] NOT APPLICABLE

BACKGROUND

[0003] The human body is replete with bioelectrical events that signal health and disease, such as neural activity (e.g., epilepsy), skeletal or cardiac neuromuscular activity (e.g., multiple sclerosis, cardiac abnormalities), eye function (macular disease), and gastrointestinal movements. Additionally, tissue properties may affect bioelectric fields (e.g., by altering permittivity) in ways that signal health and disease, e.g. hematomas, tissue edema, etc. Yet never has a technique existed to isolate and localize these informative events with high spatiotemporal certainty.

[0004] Currently, electroencephalography (EEG) and magnetoencephalography (MEG) can be used to infer the approximate location of neural events in a brain by solving what is called an inverse solution. Inverse solutions must work backward from a relatively small number of recording channels (e.g. hundreds) to isolate and localize neural activity at many more possible locations in the brain (e.g. thousands or tens of thousands). Therefore the first fundamental limitation of EEG/MEG source localization is that it is mathematically underdetermined - it literally cannot be solved. Consequently one must impose gross assumptions about the tissue properties of the head and strict, often physiologically unfounded, constraints on the mathematical solution. Though electrical source localization with MEG is presently more precise than with EEG, both suffer from great uncertainty and bias in isolating neural activity. A second major limitation of EEG/MEG as it is currently employed is that it presumably cannot use the time-of-arrival of the electric/magnetic fields at or near the scalp; rather it can only use field amplitude or variance (so called Received Signal Strength, or RSS). This also powerfully limits the utility of the EEG/MEG. Finally, MEG presently requires large, complex, expensive equipment, thus limiting broad use.

[0005] As evidence of EEG/MEG’ s limitations, consider epilepsy - one of the most common and critical applications for bioelectrical localization. Presently in cases of intractable, drug-resistant epilepsy, surgeons must remove a portion of the brain that causes the aberrant activity. EEG/MEG is not routinely used to identify the tissue for resection because it lacks precision and certainty. Consequently, surgeons must cut one large hole or many small holes in the patient’s skull, place an array of electrodes on the surface of (dura) or deep in the brain (stereotactic EEG, or sEEG), and record electrical activity for many days in hopes of identifying the offending bit of neural tissue. Thus, the state-of-the-art medical practice is highly invasive, expensive, and still inaccurate (when the aberrant tissue is not located in very close proximity to the recording electrodes).

[0006] Another leading imaging technique is functional magnetic resonance imaging (fMRI), specifically blood oxygenation level dependent (BOLD) fMRI, which is a true 3- dimensional technique (no inverse solution is required). BOLD fMRI can localize correlates of neural activity at the millimeter scale. However it is generally limited in temporal resolution to the scale of seconds by the sluggish nature of the hemodynamic response. Furthermore, much like MEG, fMRI presently also requires very large, complex, and expensive equipment, thus limiting broad use.

[0007] A further challenge inherent to detecting signals from a specific bodily location is removing interfering signals from other sources, known as noise or artifacts. Artifacts in biosignal recordings arise from numerous sources within the body (other organs or parts of the organ, e.g., cardiac signals in brain recordings), on the body (skin potentials), adjacent to or implanted in the body (electromagnetic interference from nearby computers), and from the sensor-body or sensor-environment interface (e.g., body movement artifacts). Present techniques such as EEG have poor signal-to-noise ratio in large part due to such artifacts. With the limited ability to separate and localize electrical events in the body, artifacts remain a persistent barrier to clinical and consumer use.

[0008] Source-localization algorithms using time of arrival or time-difference of arrival have been developed in numerous domains including radar, sonar, wireless networks, acoustic ranging, the global positioning system (GPS), and earthquakes. However, these tools have never been adapted for use in the domain of electromagnetic biosignals. The evident absence of such techniques follows from two common assumptions. First, all present biological electromagnetic sensing techniques treat field propagation speed as infinite: i.e. time-of-arrival at all sensors is taken to be instantaneous with the source event time. Second, biological events are rarely sampled at the rate necessary to use time-difference-of-arrival (e.g., Gigahertz, or 1,000,000,000 Hz), because the power in biological events tends to reside at relatively low frequencies (mostly less than 1,000 Hz) and, therefore, high sample rates are thought to yield no useful information.

[0009] A need exists in the art for improved spatiotemporal sensing of electrical events within human and other animal bodies.

BRIEF SUMMARY

[0010] Systems and methods perform bioelectric isolation and localization using Time- Difference-of-Arrival (TDOA). TDOA is used to isolate bioelectrical events within a brain or other parts of a body. Sensors are located in an EEG-like cap or other headpiece, or in any other spatially distributed configuration on the surface and/or within the body. The sensor and analog-to-digital converters feed data to computers that compare timing of signals among the sensors to determine bioelectrical source events in near real-time.

[0011] In some embodiments, a computing device receives, via a first sensor disposed on or in a body, a first time value associated with electrical activity in the body. The computing device receives, via a second sensor disposed on or in the body, a second time value associated with the electrical activity in the body. The computing device computes, based on the first time value and the second time value, a time difference of arrival between the first sensor and the second sensor. Based on the computed time difference of arrival, the computing device identifies a location from which the electrical activity in the body originated. The computing device performs an action based on the identified location.

[0012] In some aspects, the location is further identified based on an electromagnetic propagation speed in a medium in the body. In some aspects, performing the action comprises causing motion of a prosthetic based on the identified location.

[0013] In some aspects, the location is associated with a dysfunction, and performing the action comprises displaying the location of the dysfunction for use by a physician to remove or treat tissue in the location.

[0014] In some aspects, identifying the location from which the electrical activity in the body originated comprises applying an algebraic method for localization based on the time difference of arrival, a location of the first sensor, and a location of the second sensor.

[0015] In some aspects, identifying the location from which the electrical activity in the body originated comprises applying a trained machine learning model to output the location. In some aspects, the location is identified with a resolution of less than 2 millimeters (mm).

[0016] In some aspects, the computing system further receives, via a third sensor disposed on or in the body, a third time value associated with the electrical activity in the body, and receives, via a fourth sensor disposed on or in the body, a fourth time value associated with the electrical activity in the body, wherein the time difference of arrival is further between the third sensor and the fourth sensor and computed based on the third time value and the fourth time value.

[0017] In some aspects, a first analog-to-digital converter is coupled to the first sensor and the computing device and a second analog-to-digital converter is coupled to the second sensor and the computing device. The first time value and the second time value are received via the first analog-to-digital converter and the second analog-to-digital converter.

[0018] In some aspects, the computing system further introduces an extrinsic, non- biological electromagnetic event as a reference and determines absolute time of arrival information based on a plurality of times associated with the reference. The location is further computed based on the absolute time of arrival information. [0019] In various embodiments, a system is provided, including the first sensor, the second sensor, and the computing device, comprising one or more processors and a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, perform all or part of the operations and/or methods disclosed herein.

[0020] In various embodiments, one or more non-transitory computer-readable media are provided for storing instructions which, when executed by one or more processors, cause a system to perform part or all of the operations and/or methods disclosed herein.

[0021] A further understanding of the nature and the advantages of the inventions disclosed herein may be realized by reference of the remaining portions of the specification and the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more fully understand the present invention, reference is made to the accompanying drawings. Understanding that these drawings are not to be considered limitations in the scope of the invention, the presently described embodiments and the presently understood best mode of the invention are described with additional detail through use of the accompanying drawings.

[0023] FIG. 1A, FIG. IB, FIG. 1C, and FIG. ID show an overview of intracranial localization techniques according to some embodiments.

[0024] FIG. 2 shows a simplified block diagram of an example system for bioelectric isolation and localization according to various embodiments.

[0025] FIG. 3 is a simplified flowchart of a method for bioelectric isolation and localization, according to some embodiments.

[0026] FIG. 4 illustrates localization of neural function using stereotactic EEG according to some embodiments.

[0027] FIG. 5 illustrates cardiac imaging for diagnosis according to some embodiments.

[0028] FIG. 6 illustrates an application of skeletal muscle electromyography (EMG) bodymachine interface according to some embodiments. [0029] FIG. 7A is a first view illustrating localization of cardiac function according to some embodiments.

[0030] FIG. 7B is a second view illustrating localization of cardiac function according to some embodiments.

[0031] FIG. 8A, FIG. 8B, and FIG. 8C illustrate localization of epileptic or other bioelectric activity when multiple events or trials are averaged to improve signal-to-noise ratio according to some embodiments.

[0032] FIG. 9 is a block diagram illustrating an example computer system, according to at least one embodiment.

DETAILED DESCRIPTION

[0033] Embodiments of the present invention use time-difference-of-arrival (TDOA) information from electrical and/or magnetic field propagation to isolate bioelectrical events within the brain, heart, skeletal muscles, and other parts of the body with high spatiotemporal precision and accuracy.

[0034] As noted above, first, all present biological electromagnetic sensing techniques treat field propagation speed as infinite, i.e., time-of-arrival at all sensors is taken to be instantaneous with the source event time. Second, biological events are rarely sampled at the rate necessary to use time-difference-of-arrival (e.g., Gigahertz, or 1,000,000,000 Hz), because the power in biological events tends to reside at relatively low frequencies (mostly less than 1,000 Hz) and, therefore, high sample rates are thought to yield no useful information. Here we demonstrate that both of these long-held assumptions are unfounded. Techniques described herein treat signal propagation as finite - namely the speed of light through tissue - and use information from high sample rates to localize relatively low- frequency biological events.

[0035] One such embodiment comprises an array of multiple sensors, typically located on or near the body surface (similar to electroencephalography, magnetoencephalography, and electrocardiography (ECG, EKG)) or within the body (similar to intracranial EEG (iEEG) including brain-surface electrocorticography (ECoG) or stereotactic EEG (sEEG)), high speed analog-to-digital converters (e.g. 10 Gigahertz per second), and mathematical techniques that compare signals among the sensors. By comparing the relative timing and, optionally, the relative amplitudes and filtering of the signals, the technique isolates bioelectrical source events in time and space. Embodiments can add millisecond resolution to the best current three-dimensional imaging techniques (e.g., functional magnetic resonance imaging, or fMRI). The techniques described herein can further address otherwise insurmountable biases and uncertainties in the best current electromagnetic imaging techniques (electroencephalography EEG, and magnetoencephalography MEG). Some embodiments solve these challenges and others by using time-difference of arrival information from bioelectric fields.

[0036] FIGs. 1 A - ID illustrate an overview of intracranial localization techniques according to various embodiments. In the example illustrated in FIGs. 1 A - ID, the localization techniques are applied to detect a signal originating in the head 102 of a subject.

[0037] In the example depicted in FIG. 1 A, four sensors in the form of recording electrodes are arrayed on the scalp, two of which can be seen in FIG. 1 A. A first recording electrode 104 is disposed on the forehead. A second recording electrode 106 is disposed about 7 cm above the inion. Additional recording electrodes (not shown) may be positioned, such as a third electrode on the left MASTOID and a fourth electrode on the right mastoid. A ground electrode may further be included, e.g., on the right neck (not shown).

[0038] To perform the intracranial localization technique, neural impulses that represent epileptic activity (which may be actual or simulated) originate from one or more locations within the mouth, such as at a location 108 under the right lip as depicted in FIG. 1 A. For example, simulated pulses are applied, including a 200 ns pulse or a 400 Hz square wave.

[0039] Using the electrodes, including the first recording electrode 104 and the second recording electrode 106, scalp signals are recorded. For example, signals received by the recording electrodes 104, 106, etc. may be recorded at either 1 Gigabit per second (GSPS) or 10 GSPS. The TDOA of the simulated neural activity recorded at 10 GSPS theoretically allows accurate localization on the order of millimeters.

[0040] In the example depicted in FIG. 1 A, the first recording electrode 104 receives the signal with a delay of Atl. The second recording electrode 106 receives the signal with a delay of At2. The difference in the delays is computed. This difference is the TDOA between the second recording electrode 106 and the first recording electrode 104, given by: TDOA 2, 1) = At2 - Atl

= 11.1 nanoseconds (1.1 x 10 ⁹ seconds).

[0041] Using the TDOA between the electrodes, a location of origin of the signal is computed. The location can be determined using techniques including localization algorithms and/or machine learning models, as described in further detail with respect to FIG. 3. The estimated source location of the signal 110 is shown from a top view in FIG. IB, a front view in FIG. 1C, and a side view in FIG. ID.

[0042] FIG. 2 illustrates a simplified block diagram of an example system 200 for bioelectric isolation and localization, according to various embodiments. The system 200 includes an array of sensors 202A, 202B, 202C, and a set of analog-to-digital (A/D) converters 204A, 204B, 204C. The array of sensors 202A, 202B, and 202C is disposed on or implanted in the body of a subject 210, such as a human, primate, or other animal. The system 200 further includes a computing device 206.

[0043] In the example shown in FIG. 2, the system 200 includes three sensors, 202A, 202B, and 202C. This is one example, and various numbers of sensors can be used. The system 200 should include at least two sensors. In some implementations, three or more sensors are used for three-dimensional imaging. As further described below with respect to FIG. 3, in some aspects, four or more sensors are used.

[0044] The sensors 202 A, 202B, and 202C may be or may include electrodes. For example, sensor 202A may correspond to an electrode or an electrode probe including multiple electrodes. In various implementations, the sensors 202 can be disposed on or in the skull, chest, arm, leg, or any other suitable part of the body, as further described below.

[0045] The sensors 202A, 202B, 202C are separated in space in order to be sensitive to TDOA. The sensors 202A, 202B, 202C may sense electrical fields as with EEG, magnetic fields as with MEG, or both. The sensors 202 may detect electrical or magnetic fields with one or more physical instantiations. For example, the sensors 202 may use direct electrical connection, superconducting quantum interference device magnetometer, and/or an optically pumped magnetometer. [0046] The sensors 202 may be in contact with the body surface (as with EEG). Alternatively, or additionally, sensors 202 are situated near the body but not touching it (as with MEG). Alternatively, or additionally, sensors 202 are placed at locations within the body or its cavities (as with sEEG).

[0047] The A/D converters 204A, 204B, 204C may be configured to sample the electrical or magnetic fields. Each channel may have one A/D converter or multiple A/D converters co-localized. In the example illustrated in FIG. 2, there is one A/D converter communicatively coupled to each of the sensors (e.g., A/D converter 204A is coupled to sensor 202A, A/D converter 204B is coupled to sensor 202B, and A/D converter 204C is coupled to sensor 202C. In other implementations, multiple A/D converters 204 can be communicatively coupled to each sensor 202.

[0048] In some aspects, the A/D converters 204 A, 204B, 204C are high-speed A/D converters. For example, sampling takes place at a high enough rate to detect electrical or magnetic field propagation among channels (e.g., 10 Gigahertz per second). With the advent of LIDAR and similar technologies, ultra high-speed electronics are now sensitive to such time differences.

[0049] In some aspects, the sampling is temporally coordinated or synchronized across channels. Sampling may be interleaved in time across A/D converters 204A, 204B, 204C to achieve higher temporal resolution. Alternately, rather than A/D converters 204A, 204B, 204C sampling the fields themselves, analog circuits can embody the front-end mathematical operations to determine TDOA.

[0050] The computing device 206 may be configured to carry out the algorithms to isolate and localize bioelectrical events. The computing device 206 includes one or more processors and a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, perform one or more of the operations described herein. The computing device 206 may, for example, be a desktop computer, laptop computer, smartphone, tablet, oscilloscope, or other suitable device. The computing device 206 can be implemented in a computing system, such as the computing system 900 illustrated and described in further detail below with respect to FIG. 9. [0051] The computing device 206 is communicatively coupled to the sensors 202A, 202B, 202C and the A/D converters 204A, 204B, 204C. The computing device 206 may be communicatively coupled to the sensors 202A, 202B, 202C and the A/D converters 204A, 204B, 204C via a wired or wireless connection, using means such as physical wires, short- range communication protocols, long-range communication protocols, or any suitable means of communication.

[0052] FIG. 3 is a simplified flowchart of a method 300 for bioelectric isolation and localization, according to some embodiments. The method presented in FIG. 3 and described below is intended to be illustrative and non-limiting. It is appreciated that the processing steps may be performed in an order different from that depicted in FIG. 3 and that not all the steps depicted in FIG. 3 need be performed. In certain implementations, the method 300 may be implemented by a system such as shown in FIG. 2 and FIG. 9.

[0053] At step 302, a first time value associated with electrical activity in the body is received via a first sensor disposed on or in the body. The first sensor may receive a time value associated with receipt of an electric or magnetic impulse. The signals may, for example, include neural signals from the brain, cardiac signals from the heart, signals from the muscles, or any other suitable signals generated in association with electrical activity in the body.

[0054] At step 304, a second time value associated with electrical activity in the body is received via a second sensor disposed on or in the body. The second sensor may record a time value associated with receipt of an electric or magnetic impulse, in a similar fashion as described above with respect to step 302. The time value associated with the second sensor receiving the signal may differ from the time value associated with the first sensor receiving the signal, due to the spatial difference in distance between the origin of the signal, and the finite propagation speed of electromagnetic waves in the body.

[0055] In a similar fashion, additional time values associated with electrical activity in the body may be received via additional sensors disposed on or in the body. A third time value may be received via a third sensor, a fourth time value may be received via a fourth sensor, a fifth time value may be received via a fifth sensor, and so forth. Each time value may be different from the other time values. [0056] In some aspects, each of the time values is received by the computing device 206 via the sensors 202. For example, each sensor detects a signal, and sends an analog signal to an A/D converter 204. The A/D converter 204 converts the analog signal to a digital signal and transmits the digital signal to the computing device 206.

[0057] At step 306, a time difference of arrival (TDOA) between the first sensor and the second sensor is computed. Computing the TDOA can include computing the time it takes for the signal to reach each sensor. For example, two or more sensors receive the signal at their respective locations. The time it takes to receive the signal at each of the sensors is recorded (e.g., on the computing device 206). The difference in arrival time at each sensor is recorded (e.g., on the computing device 206). In implementations using three or more sensors, the TDOA may further be computed based on times of arrival at the third sensor, fourth sensor, fifth sensor, etc.

[0058] The TDOA of electrical and/or magnetic fields received by the sensors may be recorded. In some aspects, the computing device 206 further records, via information received via the sensors and the A/D convertors, the amplitude of the electrical and/or magnetic fields (e.g., the Received Signal Strength or RSS).

[0059] At step 308, based on the computed TDOA, a location from which the electrical activity in the body originated is identified. The computing device 206 may use the difference in arrival times identified at step 306 to calculate the distance of the source from each sensor. For example, the intersection of lines representing the distance from each sensor indicates the location of the source of the electrical activity in the body.

[0060] In some examples, the computing device 206 uses a multilateration algorithm to compute the loacation from which the electrical activity in the body originated. In some examples, the algorithm for localization follows Bancroft, S., “An Algebraic Solution of the GPS Equations,” in IEEE Transactions on Aerospace and Electronic Systems, AES-27(1), 56-59 (1985), available at https://doi.Org/10.l 109/TAES.1985.310538. Using the Bancroft algorithm, an algebraic method is used for localization based on signal arrival times and the location of the sensors (e.g., based on the time difference of arrival, a location of the first sensor, and a location of the second sensor). In some aspects, when using the Bancroft algorithm, four or more sensors may be implemented. [0061] Alternatively, or additionally, machine learning may be implemented to identify the location from which the electrical activity in the body originated. For example, given the locations of a plurality of sensors and the time of signal arrival of each signal, a model such as a neural network can be trained and applied to output the location of origin of the electrical activity in the body. The trained machine learning model may take as input the time difference of arrival, a location of the first sensor, and a location of the second sensor (and any additional sensors), and output the location.

[0062] In various implementations, the computing device 206 may implement signal separation and localization algorithms and metrics including but not limited to: correlation and coherence based measures; maximum likelihood and generalized cross-correlation; spherical interpolation and spherical intersection; array processing, both far field and near field; time-domain beamforming; frequency-domain beamforming; direction-of-arrival (DOA) estimation; hyperbolic localization and multilateration;

Bayesian estimation;

Cramer-Rao Lower Bound approaches;

Carter's focused beamforming;

Kronecker Product Decomposition; particle-based localization algorithm;

Hahn's method for passive arrays; neural networks and deep neural networks, including but not limited to transformers, convolutional neural networks, deep clustering neural networks and graph-based networks; blind-source separation approaches (e.g. Independent Component Analysis); subspace based techniques (e.g. Multiple Signal Classification or MUSIC);

• hybrid measures incorporating multiple algorithms; or

• hybrid TDOA-RSS algorithms.

[0063] In some aspects, the TDOA techniques account for the permittivity and electrical properties of the medium (e.g., brain). For example, mapping of tissue properties that correlate with electromagnetic propagation speed and calibration of TDOA measurements are implemented. Such mappings may use pairwise or multi-way comparisons among different channels. Alternatively, or additionally, such mappings may use 1-D, 2-D, or 3-D reconstruction of tissue properties and events (e.g. tomography).

[0064] In some aspects, an extrinsic, non-biological electromagnetic event is introduced as a known reference. This facilitates the use of absolute Time-of-Arrival (TOA) information as well as TDOA. For example, absolute time of arrival information is determined based on a plurality of times associated with the reference (e.g., time of arrival at each sensor). The location is further computed at step 308 based on the absolute time of arrival information.

[0065] The location can be identified for various applications. For example, the location is identified to isolate one bioelectric event from others. Alternatively, or additionally, the location is identified to determine the location of each bioelectric event. As additional applications, the techniques of FIG. 3 can be used to track the location of a moving electrical source or multiple sources, as in muscle or neural tissue; to assess the spatial extent of a bioelectric event; and/or to monitor a specific location for any bioelectric activity.

Alternatively, or additionally, the techniques of FIG. 3 can be used to scan multiple locations for bioelectric activity. Alternatively, or additionally, the techniques of FIG. 3 can be used to remove from the desired signal artifacts arising from interfering sources.

[0066] At step 310, an action is performed based on the identified location. Such actions can include displaying information about the location (e.g., for use by a clinician), controlling prosthetics, brain signal decoding, and many others including the applications listed below.

[0067] In some examples, performing the action comprises causing motion of a prosthetic based on the identified location. A configuration such as that depicted in FIG. 6 can be used to localize electrical impulses in a location such as the arm, which are decoded to control movement of a prosthetic.

[0068] In some examples, the location is associated with a dysfunction, and performing the action comprises displaying the location of the dysfunction for use by a physician to remove or treat tissue in the location. For example the location is an epileptogenic zone, dysfunctional cardiac tissue, or the like. The system can display the location information to a surgeon, which is used by the surgeon to remove or otherwise treat the associated dysfunctional tissue.

[0069] Potential applications include all bioelectric signals, not limited to:

• biosignals decoding and classification;

• epilepsy localization and monitoring;

• precise guidance for surgical removal of epileptogenic zone, to maximize clinical outcome (cessation of seizures) and to minimize the volume of tissue removed, thereby preserving function;

• closed-loop neural modulation (e.g. epilepsy, Post-Traumatic Stress Disorder (PTSD));

• neuroimaging;

• improving and complementing electrical source algorithms that rely solely on signal strength or variance such as MNE, dSPM, LORETA, sLORETA, eLORETA, LCMV beamformers, irMxNE, MuSiC, Gamma Map, SESAME and dipole fitting;

• estimation and accommodation of biosignal delays and errors including but not limited to propagation speed errors, clock biases and offsets, and multipath errors;

• neuromuscular function and dysfunction;

• cardiac function and dysfunction (e.g. cardiac arrest, atrial fibrillation, tachycardia, bradycardia, ischemia, myocardial infarction, left bundle branch block, right bundle branch block, atrioventricular block, ventricular hypertrophy, pericarditis, myocarditis) using surface electrocardiography (ECG), intracardiac ECG (iECG), and noninvasive electrocardiographic imaging (ECGi), in order to guide treatment such as pacemaker implantation, catheter ablation, defibrillation, or heart medication; • evaluation of respiration and lung muscle function, for instance in chronic obstructive pulmonary disease (COPD);

• guidance and evaluation of neural and tissue stimulation, including Deep Brain Stimulation (DBS), Transcranial Magnetic Stimulation (TMS), Transcranial Direct Current Stimulation (tDCS), Transcranial Alternating Current Stimulation (tACS), Random Noise Stimulation (RNS), Vagal Nerve Stimulation (VNS), Galvanic Vestibular Stimulation (GVS), ultrasound stimulation such as Transcranial Focused Ultrasound (tFUS) and Transcranial Pulse Stimulation (TPS), Transdermal Electrical Nerve Stimulation (TENS), Neuromuscular Electrical Stimulation (NMES), and Electrical Muscle Stimulation (EMS); eye health (e.g. macular disease); vocal tract function; swallowing function; gastrointestinal motility; tissue status (e.g. edema); hematoma detection; tumor detection; detection of neuropathy, including sensory, motor, and sensorimotor (e.g. in diabetes); presurgical determination of laterality of function (language); brain-computer-interfaces, both open- and closed-loop; brain-machine-interfaces, both open- and closed-loop; body-computer-interfaces, both open- and closed-loop; body-machine-interfaces, both open- and closed-loop; prosthetic control, both open- and closed-loop; robotic and vehicular control, both open- and closed-loop; user interfaces for digital content including phones, augmented reality (AR), virtual reality (VR), mixed reality (MR), video games, and telepresence; • biomarkers or metrics for research, diagnosis, treatment, and tracking progression of cardiac health;

• hearing screening, for instance infant hearing screening using the auditory brainstem response (ABR) and other evoked potentials;

• biomarkers or metrics for research, diagnosis, treatment, and tracking progression of perceptual, cognitive, attentional, mood/emotional/affective, psychological, and psychiatric conditions, disorders, diseases, and injuries including but not limited to Alzheimer’s disease, traumatic brain injury (TBI) or diffuse axonal injury (DAI), concussion, stroke, mild cognitive impairment (MCI), dementia, Post-Traumatic Stress Disorder (PTSD), depression, anxiety, Attention-Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, and Central Auditory Processing Disorder;

• biomarkers or metrics for research, diagnosis, treatment, and tracking progression of muscular and neuromuscular function (e.g. multiple sclerosis, Parkinson’s Disease, Amyotrophic Lateral Sclerosis, sarcopenia);

• biomarkers or metrics for research, diagnosis, treatment, and tracking progression of other bioelectric organ system function (e.g. eye, gastrointestinal system);

• decoding perception, memory, language, decision making, conscious and unconscious thought, and intended action from neural activity;

• decoding intended or imagined speech-language output;

• health and fitness monitoring;

• athletic training and optimization;

• sleep monitoring;

• lie detection;

• consumer experience and marketing;

• neural recordings from implanted or surface arrays: individuating action potentials from different neurons (“spike sorting”) or local field potentials (LFPs) from different groups of neurons, and determining depth and layer specificity of neural activity in cortex; • tissue permittivity mapping or tomography, using an introduced extrinsic, non-biological electromagnetic event as a known reference;

• improving accuracy, calibration, and utility of methods that characterize electrical properties of tissue without using TDOA, such as Electrical Impedance Tomography (EIT), Electrical Impedance Spectroscopy (EIS), Electrical Impedance Tomography Spectroscopy (EITS) and Magnetic Resonance Electrical Impedance Tomography (MREIT);

• improving tissue physical models (e.g. conductivity, permittivity) for RSS-based source localization methods; and

• biometric identification and screening.

[0070] FIGs. 4 - 8 illustrate various such implementations, including stereotactic EEG (FIG. 4), cardiac imaging (FIG. 5), skeletal muscle EMG (FIG. 6), localization of cardiac function (FIG. 7A and FIG. 7B), and of localizing epileptic bioelectric activity when multiple events or trials are averaged (FIGs. 8A - 8C).

[0071] FIG. 4 is a diagram 400 illustrating an example application including localization of neural function using stereotactic EEG (SEEG), according to various embodiments. SEEG is a procedure for identifying the source of epileptic seizures. SEEG typically uses 5-15 electrode shafts 402 with 8-18 contacts on each shaft, with locations distributed across a large intracranial volume 404. In some examples, the system for SEEG includes about 150 intracranial recording sites. Electrical TDOA can be used as described herein to localize healthy or aberrant neural activity with high precision.

[0072] FIG. 5 is a diagram 500 illustrating an example application including cardiac imaging for diagnosis, according to various embodiments. An electrode array 502 is spatially distributed on the trunk 504, much like ECG. Electrical TDOA as described herein is applied to enable distinction among healthy and abnormal electrical impulses from highly specific areas of the heart. For example, TDOA can be used to identify precise locations within each of the chambers (ventricles, atria). This would enable far greater specificity and accuracy than non-TDOA ECG, which relies primarily on the gross features of the P wave, QRS complex, and T wave. As shown in FIG. 5, at 506, a plurality of voltage vs. time values are recorded, and multilateration of individual components including P, QRS complex, and T is performed. At 508, spatiotemporal electrical cardiac imaging is performed using TDOA. [0073] FIG. 6 is a diagram 600 illustrating an example application of a skeletal muscle EMG body -machine interface, according to various embodiments. In an individual who uses an upper-limb prosthesis 610, an electrode array 602 overlies musculature 604 that can be used to control the prosthetic 610 movements. Electrical TDOA can be used to derive highly specific and sensitive control signals that enable much finer motor control than is possible with typical non-TDOA surface EMG. As shown in FIG. 6, this can include, at 606, recording a plurality of voltage vs. time values and decoding 2D or 3D muscle activation during hand/arm movement and gestures such as pointing, pinching, typing, etc.

[0074] FIG. 7A and FIG. 7B illustrate an example application of localization of cardiac function. FIG. 7A illustrates a view 700 of the chest area from the upper right of the torso, and FIG. 7B illustrates a view 750 of the chest area from below the torso.

[0075] Referring to FIG. 7A, four electrodes 702 are disposed on the surface of the left chest 704 of the individual. A reference electrode (not shown) is placed on the upper spine. In some aspects, a driven-right-leg (DRL) circuit is used to reduce common mode noise, with the DRL electrode placed on the left leg, and two common mode sense electrodes placed on the shoulders.

[0076] In some examples, for cardiac localization, ECG is recorded at 5 Gigasamples per second, using front-end bandpass filters sensitive to ECG power (around approximately 25 Hz, with 40 dB/octave attenuation). In some examples, the ECG is split into overlapping frames of 100 ms duration. The peak of the cross-correlation function is then used to identify the relative lag or time delay among the four recording electrodes. These delays are entered into an algorithm, such as the Bancroft (1985) algorithm described above, to localize heart muscle activity within each frame of time. For the purposes of illustration, the chest 704 wall is cut away to reveal the heart activity locations 706 in the interior center-left chest region, identified over multiple frames of the QRS complex. FIG. 7A shows the heart activity locations 706 from the right of the torso, and FIG. 7B shows the heart activity locations 706 from below the torso.

[0077] FIG. 8A, FIG. 8B, and FIG. 8C illustrate an example application of TDOA to localize epileptic or other bioelectric activity when multiple events or trials are averaged to improve signal -to-noise ratio. This an important constraint for some biosignals such as most neural evoked responses.

[0078] FIG. 8A is a graph 800 illustrating the results of a computational simulation of high frequency oscillations (HFO) that characterize the epileptogenic zone. The graph 800 illustrates amplitude vs. time for the HFOs. In this simulation, ten sets of oscillations were simulated. The HFOs were simulated with a sinusoid (frequency centered around 380 Hz) tapered with a Gaussian window function.

[0079] FIG. 8B illustrates the averaging 820 of the HFOs. Averaging across a set of events is shown to improve signal-to-noise ratio.

[0080] FIG. 8C illustrates the TDOA process 850 according to FIGs. 8A and 8B. The simulated HFOs were produced with a location in the left medial temporal lobe of the brain, and the simulated fields were propagated to the scalp using a boundary element model (BEM) with known propagation delays imposed. Four scalp electrodes 860 A, 860B, 860C, 860D placed as shown in FIG. 8C are used to record the simulated activity at 10 Gigasamples per second. As in the example described above with respect to FIGs. 7A and 7B, crosscorrelation is used to identify the relative timing lags among channels, and the Bancroft (1985) algorithm is used to localize the averaged HFO, within millimeter accuracy at an identified location 880 in the left temporal lobe.

[0081] The techniques described herein provide improved spatial and temporal resolution for electrical activity in the body using TDOA. These techniques can be applied invasively or noninvasively. Experimental results indicate that resolution on the order of 1 mm (e.g., a resolution less than 3 millimeters (mm), a resolution less than 2mm, or a resolution less than 1 mm) can be achieved, providing more than an order of magnitude improvement over prior techniques. Bias is also reduced due to the lack of assumptions that are typically needed for inverse solution methodologies.

[0082] FIG. 9 illustrates an example computer system 900, in which various embodiments may be implemented. The system 900 may be used to implement any of the computer systems and/or devices described above. As shown in the figure, computer system 900 includes a processing unit 904 that communicates with a number of peripheral subsystems via a bus subsystem 902. These peripheral subsystems may include a processing acceleration unit 906, an I/O subsystem 908, a storage subsystem 918 and a communications subsystem 924. Storage subsystem 918 includes tangible computer-readable storage media 922 and a system memory 910.

[0083] Bus subsystem 902 provides a mechanism for letting the various components and subsystems of computer system 900 communicate with each other as intended. Although bus subsystem 902 is shown schematically as a single bus, alternative embodiments of the bus subsystem may utilize multiple buses. Bus subsystem 902 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. For example, such architectures may include an Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, which can be implemented as a Mezzanine bus manufactured to the IEEE P1386.1 standard.

[0084] Processing unit 904, which can be implemented as one or more integrated circuits (e.g., a conventional microprocessor or microcontroller), controls the operation of computer system 900. One or more processors may be included in processing unit 904. These processors may include single core or multicore processors. In certain embodiments, processing unit 904 may be implemented as one or more independent processing units 932 and/or 934 with single or multicore processors included in each processing unit. In other embodiments, processing unit 904 may also be implemented as a quad-core processing unit formed by integrating two dual-core processors into a single chip.

[0085] In various embodiments, processing unit 904 can execute a variety of programs in response to program code and can maintain multiple concurrently executing programs or processes. At any given time, some or all of the program code to be executed can be resident in processor(s) 904 and/or in storage subsystem 918. Through suitable programming, processor(s) 904 can provide various functionalities described above. Computer system 900 may additionally include a processing acceleration unit 906, which can include a digital signal processor (DSP), a special-purpose processor, and/or the like.

[0086] I/O subsystem 908 may include user interface input devices and user interface output devices. User interface input devices may include a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. User interface input devices may include, for example, motion sensing and/or gesture recognition devices such as the Microsoft Kinect® motion sensor that enables users to control and interact with an input device, such as the Microsoft Xbox® 360 game controller, through a natural user interface using gestures and spoken commands. User interface input devices may also include eye gesture recognition devices such as the Google Glass® blink detector that detects eye activity (e.g., ‘blinking’ while taking pictures and/or making a menu selection) from users and transforms the eye gestures as input into an input device (e.g., Google Glass®). Additionally, user interface input devices may include voice recognition sensing devices that enable users to interact with voice recognition systems (e.g., Siri® navigator), through voice commands.

[0087] User interface input devices may also include, without limitation, three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices. Additionally, user interface input devices may include, for example, medical imaging input devices such as computed tomography, magnetic resonance imaging, position emission tomography, medical ultrasonography devices. User interface input devices may also include, for example, audio input devices such as MIDI keyboards, digital musical instruments and the like.

[0088] User interface output devices may include a display subsystem, indicator lights, or non-visual displays such as audio output devices, etc. The display subsystem may be a cathode ray tube (CRT), a flat-panel device, such as that using a liquid crystal display (LCD) or plasma display, a projection device, a touch screen, and the like. In general, use of the term "output device" is intended to include all possible types of devices and mechanisms for outputting information from computer system 900 to a user or other computer. For example, user interface output devices may include, without limitation, a variety of display devices that visually convey text, graphics and audio/video information such as monitors, printers, speakers, headphones, automotive navigation systems, plotters, voice output devices, and modems.

[0089] Computer system 900 may comprise a storage subsystem 918 that comprises software elements, shown as being currently located within a system memory 910. System memory 910 may store program instructions that are loadable and executable on processing unit 904, as well as data generated during the execution of these programs.

[0090] Depending on the configuration and type of computer system 900, system memory 910 may be volatile (such as random-access memory (RAM)) and/or non-volatile (such as read-only memory (ROM), flash memory, etc.) The RAM typically contains data and/or program modules that are immediately accessible to and/or presently being operated and executed by processing unit 904. In some implementations, system memory 910 may include multiple different types of memory, such as static random-access memory (SRAM) or dynamic random-access memory (DRAM). In some implementations, a basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within computer system 900, such as during start-up, may typically be stored in the ROM. By way of example, and not limitation, system memory 910 also illustrates application programs 912, which may include client applications, Web browsers, mid-tier applications, relational database management systems (RDBMS), etc., program data 914, and an operating system 916. By way of example, operating system 916 may include various versions of Microsoft Windows®, Apple Macintosh®, and/or Linux operating systems, a variety of commercially available UNIX® or UNIX-like operating systems (including without limitation the variety of GNU/Linux operating systems, the Google Chrome® OS, and the like) and/or mobile operating systems such as iOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, and Palm® OS operating systems.

[0091] Storage subsystem 918 may also provide a tangible computer-readable storage medium for storing the basic programming and data constructs that provide the functionality of some embodiments. Software (programs, code modules, instructions) that when executed by a processor provide the functionality described above may be stored in storage subsystem 918. These software modules or instructions may be executed by processing unit 904. Storage subsystem 918 may also provide a repository for storing data used in accordance with the present disclosure.

[0092] Storage subsystem 918 may also include a computer-readable storage media reader 920 that can further be connected to computer-readable storage media 922. Together and optionally, in combination with system memory 910, computer-readable storage media 922 may comprehensively represent remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing, storing, transmitting, and retrieving computer-readable information.

[0093] Computer-readable storage media 922 containing code, or portions of code, can also include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to, volatile and non-volatile, removable and nonremovable media implemented in any method or technology for storage and/or transmission of information. This can include tangible computer-readable storage media such as RAM, ROM, electronically erasable programmable ROM (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible computer readable media. This can also include nontangible computer-readable media, such as data signals, data transmissions, or any other medium which can be used to transmit the desired information, and which can be accessed by computing system 900.

[0094] By way of example, computer-readable storage media 922 may include a hard disk drive that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive that reads from or writes to a removable, nonvolatile magnetic disk, and an optical disk drive that reads from or writes to a removable, nonvolatile optical disk such as a CD ROM, DVD, and Blu-Ray® disk, or other optical media. Computer-readable storage media 922 may include, but is not limited to, Zip® drives, flash memory cards, universal serial bus (USB) flash drives, secure digital (SD) cards, DVD disks, digital video tape, and the like. Computer- readable storage media 922 may also include, solid-state drives (SSD) based on non-volatile memory such as flash-memory based SSDs, enterprise flash drives, solid state ROM, and the like, SSDs based on volatile memory such as solid state RAM, dynamic RAM, static RAM, DRAM-based SSDs, magneto- resistive RAM (MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and flash memory based SSDs. The disk drives and their associated computer-readable media may provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for computer system 900.

[0095] Communications subsystem 924 provides an interface to other computer systems and networks. Communications subsystem 924 serves as an interface for receiving data from and transmitting data to other systems from computer system 900. For example, communications subsystem 924 may enable computer system 900 to connect to one or more devices via the Internet. In some embodiments communications subsystem 924 can include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular telephone technology, advanced data network technology, such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi (IEEE 802.11 family standards, or other mobile communication technologies, or any combination thereof), global positioning system (GPS) receiver components, and/or other components. In some embodiments communications subsystem 924 can provide wired network connectivity (e.g., Ethernet) in addition to or instead of a wireless interface.

[0096] In some embodiments, communications subsystem 924 may also receive input communication in the form of structured and/or unstructured data feeds 926, event streams 928, event updates 930, and the like on behalf of one or more users who may use computer system 900.

[0097] Additionally, communications subsystem 924 may also be configured to receive data in the form of continuous data streams, which may include event streams 928 of real-time events and/or event updates 930, that may be continuous or unbounded in nature with no explicit end. Examples of applications that generate continuous data may include, for example, sensor data applications, network performance measuring tools (e.g., network monitoring and traffic management applications), and the like.

[0098] Communications subsystem 924 may also be configured to output the structured and/or unstructured data feeds 926, event streams 928, event updates 930, and the like to one or more databases that may be in communication with one or more streaming data source computers coupled to computer system 900.

[0099] Computer system 900 can be one of various types, including a handheld portable device (e.g., an iPhone® cellular phone, an iPad® computing tablet, a PDA), a wearable device (e.g., a Google Glass® head mounted display), a PC, a workstation, a mainframe, a kiosk, a server rack, or any other data processing system.

[0100] Due to the ever-changing nature of computers and networks, the description of computer system 900 depicted in the figure is intended only as a specific example. Many other configurations having more or fewer components than the system depicted in the figure are possible. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, firmware, software (including applets), or a combination. Further, connection to other computing devices, such as network input/output devices, may be employed. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the various embodiments.

[0101] It should be appreciated that the computing system for bioelectric TDOA may have one or more microprocessors/processing devices that can further be a component of the overall apparatuses. The control systems are generally proximate to their respective devices, in electronic communication (wired or wireless) and can also include a display interface and/or operational controls configured to be handled by a user to monitor the respective systems, to change configurations of the respective systems, and to operate, directly guide, or set programmed instructions for the respective systems, and sub-portions thereof. Such processing devices can be communicatively coupled to a non-volatile memory device via a bus. The nonvolatile memory device may include any type of memory device that retains stored information when powered off. Non-limiting examples of the memory device include electrically erasable programmable read-only memory (“ROM”), flash memory, or any other type of non-volatile memory. In some aspects, at least some of the memory device can include a non-transitory medium or memory device from which the processing device can read instructions. A nontransitory computer-readable medium can include electronic, optical, magnetic, or other storage devices capable of providing the processing device with computer- readable instructions or other program code. Non-limiting examples of a non-transitory computer-readable medium include (but are not limited to) magnetic disk(s), memory chip(s), ROM, random-access memory (“RAM”), an ASIC, a configured processor, optical storage, and/or any other medium from which a computer processor can read instructions. The instructions may include processor-specific instructions generated by a compiler and/or an interpreter from code written in any suitable computer-programming language, including, for example, C, C++, C#, Java, Python, Perl, JavaScript, etc. Part or all of the computing system could reside in a physically remote location with communication between the local apparatus and the remote computing system conveyed via the internet (e.g. cloud computing).

[0102] While the above description describes various embodiments of the invention and the best mode contemplated, regardless of how detailed the above text, the invention can be practiced in many ways. Details of the system may vary considerably in its specific implementation, while still being encompassed by the present disclosure. As noted above, particular terminology used when describing certain features or aspects of the invention should not be taken to imply that the terminology is being redefined herein to be restricted to any specific characteristics, features, or aspects of the invention with which that terminology is associated. In general, the terms used in the following claims should not be construed to limit the invention to the specific examples disclosed in the specification, unless the above Detailed Description section explicitly defines such terms. Accordingly, the actual scope of the invention encompasses not only the disclosed examples, but also all equivalent ways of practicing or implementing the invention under the claims.

[0103] The teachings of the invention provided herein can be applied to other systems, not necessarily the system described above. The elements and acts of the various examples described above can be combined to provide further implementations of the invention. Some alternative implementations of the invention may include not only additional elements to those implementations noted above, but also may include fewer elements. Further any specific numbers noted herein are only examples; alternative implementations may employ differing values or ranges, and can accommodate various increments and gradients of values within and at the boundaries of such ranges.

[0104] References throughout the foregoing description to features, advantages, or similar language do not imply that all of the features and advantages that may be realized with the present technology should be or are in any single embodiment of the invention. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present technology. Thus, discussion of the features and advantages, and similar language, throughout this specification may, but do not necessarily, refer to the same embodiment. Furthermore, the described features, advantages, and characteristics of the present technology may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize that the present technology can be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the present technology.

[0105] Although specific embodiments have been described, various modifications, alterations, alternative constructions, and equivalents are also encompassed within the scope of the disclosure. Embodiments are not restricted to operation within certain specific data processing environments but are free to operate within a plurality of data processing environments. Additionally, although embodiments have been described using a particular series of transactions and steps, it should be apparent to those skilled in the art that the scope of the present disclosure is not limited to the described series of transactions and steps. Various features and aspects of the above-described embodiments may be used individually or jointly.

[0106] Further, while embodiments have been described using a particular combination of hardware and software, it should be recognized that other combinations of hardware and software are also within the scope of the present disclosure. Embodiments may be implemented only in hardware, or only in software, or using combinations thereof. The various processes described herein can be implemented on the same processor or different processors in any combination. Accordingly, where components or modules are described as being configured to perform certain operations, such configuration can be accomplished, e.g., by designing electronic circuits to perform the operation, by programming programmable electronic circuits (such as microprocessors) to perform the operation, or any combination thereof. Processes can communicate using a variety of techniques including but not limited to conventional techniques for inter process communication, and different pairs of processes may use different techniques, or the same pair of processes may use different techniques at different times.

[0107] The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. It will, however, be evident that additions, subtractions, deletions, and other modifications and changes may be made thereunto without departing from the broader spirit and scope as set forth in the claims. Thus, although specific disclosure embodiments have been described, these are not intended to be limiting. Various modifications and equivalents are within the scope of the following claims.

[0108] The use of the terms “a” and “an” and “the” and similar referents in the context of describing the disclosed embodiments (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. The term “connected” is to be construed as partly or wholly contained within, attached to, or joined together, even if there is something intervening. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate embodiments and does not pose a limitation on the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any nonclaimed element as essential to the practice of the disclosure.

[0109] Disjunctive language such as the phrase “at least one of X, Y, or Z,” unless specifically stated otherwise, is intended to be understood within the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.

[0110] Preferred embodiments of this disclosure are described herein, including the best mode known for carrying out the disclosure. Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. Those of ordinary skill should be able to employ such variations as appropriate and the disclosure may be practiced otherwise than as specifically described herein. Accordingly, this disclosure includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the abovedescribed elements in all possible variations thereof is encompassed by the disclosure unless otherwise indicated herein.

[OHl] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

[0112] In the foregoing specification, aspects of the disclosure are described with reference to specific embodiments thereof, but those skilled in the art will recognize that the disclosure is not limited thereto. Various features and aspects of the above-described disclosure may be used individually or jointly. Further, embodiments can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive.

Claims

WHAT IS CLAIMED IS:

1. A system comprising: a first sensor disposed on or in a body; a second sensor disposed on or in the body; and a computing device comprising one or more processors and a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, perform a method comprising: receiving, via the first sensor, a first time value associated with electrical activity in the body; receiving, via the second sensor, a second time value associated with the electrical activity in the body; computing, based on the first time value and the second time value, a time difference of arrival between the first sensor and the second sensor; based on the computed time difference of arrival, identifying a location from which the electrical activity in the body originated; and performing an action based on the identified location.

2. The system of claim 1, wherein the location is further identified based on an electromagnetic propagation speed in a medium in the body.

3. The system of claim 1, wherein performing the action comprises causing motion of a prosthetic based on the identified location.

4. The system of claim 1, wherein the location is associated with a dysfunction, and performing the action comprises displaying the location of the dysfunction for use by a physician to remove or treat tissue in the location.

5. The system of claim 1, wherein identifying the location from which the electrical activity in the body originated comprises applying an algebraic method for localization based on the time difference of arrival, a location of the first sensor, and a location of the second sensor.

6. The system of claim 1, wherein identifying the location from which the electrical activity in the body originated comprises applying a trained machine learning model to output the location.

7. The system of claim 1, wherein the location is identified with a resolution of less than 2 millimeters (mm).

8. The system of claim 1, further comprising: a third sensor disposed on or in the body; and a fourth sensor disposed on or in the body, the method further comprising: receiving, via the third sensor, a third time value associated with the electrical activity in the body, and receiving, via the fourth sensor, a fourth time value associated with the electrical activity in the body, wherein the time difference of arrival is further between the third sensor and the fourth sensor and computed based on the third time value and the fourth time value.

9. The system of claim 1, further comprising: a first analog-to-digital converter coupled to the first sensor and the computing device; and a second analog-to-digital converter coupled to the second sensor and the computing device, wherein the first time value and the second time value are received via the first analog-to-digital converter and the second analog-to-digital converter.

10. The system of claim 1, the method further comprising: introducing an extrinsic, non-biological electromagnetic event as a reference; and determining absolute time of arrival information based on a plurality of times associated with the reference, wherein the location is further computed based on the absolute time of arrival information.

11. A method comprising: receiving, by a computing device via a first sensor disposed on or in a body, a first time value associated with electrical activity in the body; receiving, by the computing device via a second sensor disposed on or in the body, a second time value associated with the electrical activity in the body; computing, by the computing device based on the first time value and the second time value, a time difference of arrival between the first sensor and the second sensor; based on the computed time difference of arrival, identifying, by the computing device a location from which the electrical activity in the body originated; and performing, by the computing device, an action based on the identified location.

12. The method of claim 1, wherein the location is further identified based on an electromagnetic propagation speed in a medium in the body.

13. The method of claim 1, wherein performing the action comprises causing motion of a prosthetic based on the identified location.

14. The method of claim 1, wherein the location is associated with a dysfunction, and performing the action comprises displaying the location of the dysfunction for use by a physician to remove or treat tissue in the location.

15. The method of claim 1, wherein identifying the location from which the electrical activity in the body originated comprises applying an algebraic method for localization based on the time difference of arrival, a location of the first sensor, and a location of the second sensor.

16. The method of claim 1, wherein identifying the location from which the electrical activity in the body originated comprises applying a trained machine learning model to output the location.

17. The method of claim 1, wherein the location is identified with a resolution of less than 2 millimeters (mm).

18. The method of claim 1, further comprising: receiving, via a third sensor disposed on or in the body, a third time value associated with the electrical activity in the body; and receiving, via a fourth sensor disposed on or in the body, a fourth time value associated with the electrical activity in the body, wherein the time difference of arrival is further between the third sensor and the fourth sensor and computed based on the third time value and the fourth time value.

19. The method of claim 1, wherein: a first analog-to-digital converter is coupled to the first sensor and the computing device; and a second analog-to-digital converter is coupled to the second sensor and the computing device; and the first time value and the second time value are received via the first analog- to-digital converter and the second analog-to-digital converter.

20. The method of claim 1, further comprising: introducing an extrinsic, non-biological electromagnetic event as a reference; and determining absolute time of arrival information based on a plurality of times associated with the reference, wherein the location is further computed based on the absolute time of arrival information.