CN104545900B - Event related potential analyzing method based on paired sample T test - Google Patents
Event related potential analyzing method based on paired sample T test Download PDFInfo
- Publication number
- CN104545900B CN104545900B CN201410836452.3A CN201410836452A CN104545900B CN 104545900 B CN104545900 B CN 104545900B CN 201410836452 A CN201410836452 A CN 201410836452A CN 104545900 B CN104545900 B CN 104545900B
- Authority
- CN
- China
- Prior art keywords
- stimuli
- significant difference
- erp
- eeg
- period
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000010831 paired-sample T-test Methods 0.000 title claims abstract description 29
- 238000000034 method Methods 0.000 title claims description 14
- 210000004556 brain Anatomy 0.000 claims abstract description 29
- 238000004458 analytical method Methods 0.000 claims abstract description 16
- 210000004761 scalp Anatomy 0.000 claims abstract description 15
- 238000007781 pre-processing Methods 0.000 claims abstract description 14
- 238000002474 experimental method Methods 0.000 claims abstract description 7
- 238000013461 design Methods 0.000 claims abstract description 6
- 230000000763 evoking effect Effects 0.000 claims description 10
- 230000000284 resting effect Effects 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 238000001784 detoxification Methods 0.000 claims description 3
- 230000004424 eye movement Effects 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000012880 independent component analysis Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 2
- 230000002269 spontaneous effect Effects 0.000 abstract description 17
- 238000012876 topography Methods 0.000 abstract description 5
- 238000011160 research Methods 0.000 abstract description 4
- 230000000638 stimulation Effects 0.000 description 4
- 238000012935 Averaging Methods 0.000 description 3
- 230000001020 rhythmical effect Effects 0.000 description 2
- 208000014644 Brain disease Diseases 0.000 description 1
- 210000004958 brain cell Anatomy 0.000 description 1
- 230000003925 brain function Effects 0.000 description 1
- 210000003169 central nervous system Anatomy 0.000 description 1
- 230000001149 cognitive effect Effects 0.000 description 1
- 230000036992 cognitive tasks Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 210000003128 head Anatomy 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000003238 somatosensory effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Veterinary Medicine (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Public Health (AREA)
- Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Psychiatry (AREA)
- Psychology (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
一种基于配对样本T检验的事件相关电位分析方法：设计含两种刺激的脑电诱发实验，并利用脑电采集设备记录多个导联的头皮脑电信号，进行初步的预处理；提取两种刺激下的ERP信号；对两种刺激下的ERP信号进行配对样本T检验，确定具有显著差异的时段；计算两种刺激下的ERP信号在显著差异时段内的差异面积，并绘制脑地形图，确定差异脑区。本发明确定了两种刺激下的ERP显著差异时段，并绘制了基于ERP波形差异面积的脑地形图，进而得到在显著差异时段内的差异脑区分布。本发明对于信噪比较差、且单个成分并不明显的ERP研究具有重要意义，并且为ERP信号和自发脑电的剥离提供了新的思路。
An event-related potential analysis method based on paired-sample T-test: design an EEG-induced experiment containing two stimuli, and use an EEG acquisition device to record scalp EEG signals from multiple leads for preliminary preprocessing; extract two ERP signals under two stimuli; Paired-sample T-test was performed on the ERP signals under two stimuli to determine the period of significant difference; the difference area of ERP signals under the two stimuli within the significant difference period was calculated, and the brain topography was drawn , to identify differential brain regions. The invention determines the significant difference period of ERP under the two stimuli, and draws a brain topographic map based on the area of ERP waveform difference, and then obtains the distribution of different brain regions in the significant difference period. The invention has great significance for the research of ERP with poor signal-to-noise ratio and indistinct individual components, and provides a new idea for the stripping of ERP signals and spontaneous EEG.
Description
技术领域technical field
本发明涉及一种事件相关电位分析方法。特别是涉及一种包含多种外界刺激、且单种刺激重复次数较少的基于配对样本T检验的事件相关电位分析方法。The invention relates to an event-related potential analysis method. In particular, it relates to an event-related potential analysis method based on paired-sample T-test that includes multiple external stimuli and has less repetitions of a single stimulus.
背景技术Background technique
脑电信号是通过电极记录下来的脑细胞群的自发性、节律性电活动，根据是否含有外部刺激，可分为自发脑电(Electroencephalo-graph,EEG)和事件相关电位(Event-related Potentials,ERP)两种。事件相关电位是人们对特定刺激事件进行感知加工或执行某种认知任务时诱发出来的一种脑电信号，常用于反映刺激发生前后脑电位的变化情况，与大脑注意资源分配、客体记忆、思维决策、认知加工等相关。由于ERP信号具有毫秒级的时间分辨率、良好的非侵入性，并且采集设备操作较简单，该信号在脑功能研究和脑疾病预诊方面都有颇多应用。EEG signals are the spontaneous and rhythmic electrical activity of brain cell populations recorded by electrodes, and can be divided into spontaneous EEG (Electroencephalo-graph, EEG) and event-related potentials (Event-related Potentials, ERP) Two kinds. Event-related potential is a kind of EEG signal induced by people when they perceive and process a specific stimulus event or perform a certain cognitive task. thinking, decision-making, and cognitive processing. Because the ERP signal has millisecond time resolution, good non-invasiveness, and the operation of the acquisition equipment is relatively simple, the signal has many applications in the study of brain function and the prediction of brain diseases.
多年来，ERP研究的一个重大难点就是与自发脑电的剥离。研究显示，大脑无时无刻不在运转，即使在不给任何外界刺激的情况下，中枢神经系统亦存在着节律性、自发性放电现象，而外部事件诱发的ERP信号幅值远小于自发脑电，且通常被淹没在自发脑电中。由于自发脑电具有很大的个体差异性和随机性，因此不能形成一个固定的自发脑电模板，使得ERP信号便于剥离。实际过程中，通常采用多次重复施加外部刺激、再求平均的方式提高ERP信号的幅值和纯度，进而将其与自发脑电剥离。为了得到信噪比较好的ERP信号，通常需要几十甚至几百次的重复外界刺激，一方面，多次重复刺激必然会引起感官系统的疲劳，且难以保持完全一致的重复性；另一方面，大量刺激材料的准备并不容易，尤其是对于特定含义的图片、声音等较复杂的刺激。Over the years, a major difficulty in ERP research has been the separation from spontaneous EEG. Studies have shown that the brain is running all the time. Even without any external stimulation, there are rhythmic and spontaneous discharges in the central nervous system, and the amplitude of ERP signals induced by external events is much smaller than that of spontaneous EEG, and usually Overwhelmed by spontaneous EEG. Since the spontaneous EEG has great individual differences and randomness, it cannot form a fixed spontaneous EEG template, which makes the ERP signal easy to strip. In the actual process, the amplitude and purity of the ERP signal are usually increased by repeatedly applying external stimuli and then averaging, so as to separate it from the spontaneous EEG. In order to obtain an ERP signal with a good signal-to-noise ratio, dozens or even hundreds of repeated external stimuli are usually required. On the one hand, repeated stimuli will inevitably cause sensory system fatigue, and it is difficult to maintain a completely consistent repeatability; On the one hand, it is not easy to prepare a large amount of stimulus materials, especially for more complex stimuli such as pictures and sounds with specific meanings.
另外，以往的ERP分析多集中于某个或者某几个ERP成分(如P1，N1，P3等)的分析，然而对于重复刺激次数较少、信噪比欠佳的ERP信号，具有明确物理意义的ERP成分往往难以识别，也造成不同刺激下ERP特征提取的困难。In addition, previous ERP analysis mostly focused on the analysis of one or several ERP components (such as P1, N1, P3, etc.), but it has a clear physical meaning for ERP signals with fewer repeated stimulation times and poor signal-to-noise ratio. It is often difficult to identify the ERP components of different stimuli, which also makes it difficult to extract ERP features under different stimuli.
基于配对样本T检验的事件相关电位分析方法从显著差异的角度分析ERP信号，能够提取出有显著意义的差异特征，避开了单个ERP成分提取的困难，是ERP对比分析的新思路。另外，由于两种不同外界刺激下的自发脑电虽不完全一致，但也不具有显著的差异性，若对两种刺激下的ERP信号进行配对样本T检验，得到的显著差异时段必为两真实ERP信号具有显著差异的时段，可以实现ERP信号与自发脑电的间接剥离。The event-related potential analysis method based on the paired sample T-test analyzes ERP signals from the perspective of significant differences, which can extract significant difference features and avoid the difficulty of extracting individual ERP components. It is a new idea for ERP comparative analysis. In addition, since the spontaneous EEG under two different external stimuli is not exactly the same, but there is no significant difference, if the paired sample T-test is performed on the ERP signals under the two stimuli, the significant difference periods obtained must be two The period of time when the real ERP signal has a significant difference can realize the indirect stripping of the ERP signal and the spontaneous EEG.
发明内容Contents of the invention
本发明所要解决的技术问题是，提供一种可用于信噪比较差、且单个成分并不明显的ERP研究的基于配对样本T检验的事件相关电位分析方法。The technical problem to be solved by the present invention is to provide an event-related potential analysis method based on paired sample T-test, which can be used for ERP research with poor signal-to-noise ratio and no obvious single component.
本发明所采用的技术方案是：一种基于配对样本T检验的事件相关电位分析方法，包括如下步骤：The technical scheme adopted in the present invention is: a kind of event-related potential analysis method based on paired sample T test, comprising the following steps:
1)设计含两种刺激的脑电诱发实验，并利用脑电采集设备记录多个导联的头皮脑电信号，进行初步的预处理；1) Design an EEG-induced experiment containing two stimuli, and use the EEG acquisition equipment to record the scalp EEG signals of multiple leads for preliminary preprocessing;
2)提取两种刺激下的ERP信号；2) Extract the ERP signals under the two stimuli;
3)对两种刺激下的ERP信号进行配对样本T检验，确定具有显著差异的时段；3) Perform a paired sample T-test on the ERP signals under the two stimuli to determine the time periods with significant differences;
4)计算两种刺激下的ERP信号在显著差异时段内的差异面积，并绘制脑地形图，确定差异脑区。4) Calculate the difference area of the ERP signal under the two stimuli within a significant difference period, and draw a brain topographic map to determine the difference brain area.
步骤1)所述的初步的预处理，是为去除头皮脑电信号记录过程中的低频漂移、高频干扰以及眼动电生理信号的干扰，对原始脑电信号进行变平均参考、0.5-10Hz带通滤波以及独立成分分析去眼电的预处理操作。The preliminary preprocessing described in step 1) is to remove the low-frequency drift, high-frequency interference and eye movement electrophysiological signal interference in the recording process of the scalp EEG signal, and perform a variable average reference, 0.5-10Hz, on the original EEG signal. Band-pass filtering and independent component analysis are preprocessing operations for electrooculogram removal.
步骤2)所述的提取两种以上刺激下的ERP信号，包括如下过程：Step 2) described extracting the ERP signal under more than two kinds of stimuli includes the following process:
(1)对初步预处理之后的整段头皮脑电信号进行分割，得到20个时长为4s的静息脑电片段和20个时长为1s的诱发脑电片段，其中，两种刺激对应的诱发脑电片段各10个；(1) Segment the entire scalp EEG signal after preliminary preprocessing to obtain 20 resting EEG segments with a duration of 4 s and 20 evoked EEG segments with a duration of 1 s. 10 EEG fragments each;
(2)选取刺激呈现之前的200ms，即静息期的后200ms为基准脑电，并计算基准脑电的平均幅值，将每个诱发脑电片段减去基准脑电平均幅值，实现去基线操作；(2) Select the 200ms before the stimulus presentation, that is, the last 200ms of the resting period as the reference EEG, and calculate the average amplitude of the reference EEG, and subtract the average amplitude of the reference EEG from each evoked EEG segment to achieve detoxification. Baseline operations;
(3)分别对去基线后的两种诱发脑电片段进行叠加平均，得到每位被试者的每个导联在两种刺激下的ERP信号，分别表示为X＝{Xijk}和Y＝{Yijk}，其中，i＝1,2,……,N1，N1＝15，N1是被试者数目；j＝1,2,……,N2，N2＝32，N2是导联数目，k＝1,2,……,N3，N3＝1024，N3是数据点数。(3) Superimpose and average the two evoked EEG segments after removing the baseline, and obtain the ERP signals of each lead of each subject under the two stimuli, which are respectively expressed as X={X ijk } and Y ={Y ijk }, wherein, i=1,2,...,N 1 , N 1 =15, N 1 is the number of subjects; j=1,2,...,N 2 , N 2 =32, N 2 is the number of leads, k=1, 2, ..., N 3 , N 3 =1024, and N 3 is the number of data points.
步骤3)中所述的对两种刺激下的ERP信号进行配对样本T检验，是对每个导联中的每个数据点对应的ERP序列进行配对样本T检验，对于第j个导联第k个数据点，首先建立一个新变量Z＝{Zijk}，Zijk＝Xijk-Yijk，i＝1,2,……,N1，计算新变量的均值和方差构造统计量检验tjk是否服从自由度为N1-1的T分布，并计算出对应的显著程度Pjk，若Pjk<0.05，则序列和序列具有显著性差异，即两种刺激下的第j个导联第k个数据点具有显著性差异，否则，两种刺激下的第j个导联第k个数据点不具有显著性差异。The paired sample T test is carried out on the ERP signals under the two stimuli described in step 3), which is to perform a paired sample T test on the ERP sequence corresponding to each data point in each lead. For the jth lead and the first k data points, first create a new variable Z={Z ijk }, Z ijk ＝X ijk -Y ijk , i=1,2,...,N 1 , calculate the mean of the new variable and variance construction statistics Test whether t jk obeys the T distribution with N 1 -1 degrees of freedom, and calculate the corresponding significance level P jk , if P jk <0.05, the sequence and sequence There is a significant difference, that is, the kth data point of the jth lead under the two stimuli has a significant difference, otherwise, the kth data point of the jth lead under the two stimuli does not have a significant difference.
步骤3)中所述的确定具有显著差异的时段，是在已确定两种刺激下的第j个导联第k个数据点是否具有显著性差异的基础上进行的，包含如下过程：The period of determining a significant difference described in step 3) is carried out on the basis of determining whether the kth data point of the jth lead under the two stimuli has a significant difference, including the following process:
(1)对于第j个导联的N3个数据点，若存在10个以上的连续数据点k，使得序列和序列具有显著性差异，那么这些连续的数据点k对应的时段就是第j个导联在两种刺激下的ERP信号具有显著差异的时段，若不存在10个以上的连续数据点k，使得序列和序列具有显著性差异，则第j个导联在两种刺激下的ERP信号不具有显著差异时段；(1) For the N 3 data points of the jth lead, if there are more than 10 continuous data points k, such that the sequence and sequence If there is a significant difference, then the period corresponding to these continuous data points k is the period when the ERP signal of the jth lead under the two stimuli has a significant difference. If there are no more than 10 continuous data points k, the sequence and sequence There is a significant difference, then the ERP signal of the jth lead under the two stimuli does not have a significant difference period;
(2)根据所有导联的显著差异时段分布，选择一个以上相对大的时段，使得尽可能包含多数导联的显著差异时段，所选的显著差异时段对应的数据点集标记为其中，r＝1,2,…,m，m为所选的显著差异时段数，可选范围为{1,2,…,100}，nr分别是每个显著差异时段所对应的数据点数，取值均大于10。(2) According to the significant difference period distribution of all leads, select more than one relatively large period, so that the significant difference period of most leads can be included as much as possible, and the data point set corresponding to the selected significant difference period is marked as Among them, r=1,2,...,m, m is the number of significant difference periods selected, the optional range is {1,2,...,100}, n r is the number of data points corresponding to each significant difference period , all values are greater than 10.
步骤4)中所述的计算两种刺激下的ERP信号在显著差异时段内的差异面积，是对每位被试者的每个导联均计算两种刺激下的ERP信号在所选显著差异时段内的差异面积，其中，在显著差异时段内的两种刺激下的ERP信号的差异面积为并对所有被试者的数据进行叠加平均，得到第j个导联在所选显著差异时段内两种刺激下的ERP信号差异面积 The calculation of the difference area of the ERP signal under the two stimuli in the significant difference period described in step 4) is to calculate the ERP signal under the two stimuli in the selected significant difference for each lead of each subject. The difference area within the period, where, in the period of significant difference The difference area of the ERP signal under the two stimuli within is And the data of all subjects were superimposed and averaged to obtain the ERP signal difference area of the jth lead under the two stimuli in the selected significant difference period
步骤4)中所述的绘制脑地形图、确定差异脑区，是对于每一个显著差异时段，均根据所有导联的两种刺激下的ERP信号差异面积绘制脑地形图，进而分析两种刺激所诱发ERP信号差异的空间分布状况，得到在显著差异时段内的主要激活脑区分布。The drawing of the brain topography map and the determination of the difference brain region described in step 4) are to draw the brain topography map according to the ERP signal difference area under the two stimuli of all leads for each significant difference period, and then analyze the two stimuli The spatial distribution of the differences in the induced ERP signals was used to obtain the distribution of the main activated brain regions during the significant difference period.
本发明的一种基于配对样本T检验的事件相关电位分析方法，从配对样本T检验出发，确定了两种刺激下的ERP显著差异时段，并绘制了基于ERP波形差异面积的脑地形图，进而得到在显著差异时段内的差异脑区分布。本发明主要针对包含多种外界刺激、且单种刺激重复次数较少的诱发脑电研究，对于信噪比较差、且单个成分并不明显的ERP研究具有重要意义，并且为ERP信号和自发脑电的剥离提供了新的思路。A kind of event-related potential analysis method based on paired sample T-test of the present invention starts from the paired-sample T-test, determines the significant difference period of ERP under two kinds of stimuli, and draws the brain topographic map based on the difference area of ERP waveform, and then The distribution of different brain regions in the significant difference period was obtained. The present invention is mainly aimed at the evoked EEG research involving multiple external stimuli and with a small number of repetitions of a single stimulus. The stripping of EEG provides new ideas.
附图说明Description of drawings
图1是一种基于配对样本T检验的事件相关电位分析方法的流程图。Fig. 1 is a flowchart of an event-related potential analysis method based on paired-sample T-test.
具体实施方式detailed description
下面结合实施例和附图对本发明的一种基于配对样本T检验的事件相关电位分析方法做出详细说明。An event-related potential analysis method based on paired sample T-test of the present invention will be described in detail below with reference to the embodiments and the accompanying drawings.
本发明的一种基于配对样本T检验的事件相关电位(Event-related Potentials,ERP)分析方法，首先利用脑电采集设备记录两种刺激下的多导头皮脑电信号，并进行初步的预处理；其次提取两种刺激下的ERP信号；再次分别对每一导联在两种外界刺激下的ERP信号进行配对样本T检验，得到每一导联ERP信号在两种刺激下的显著差异时段；最后通过计算显著差异时段内两ERP信号包围的面积，可以得到两种刺激下全脑所有导联在显著差异时段内的差异面积分布，进而通过脑地形图显示两种刺激所诱发的ERP信号差异的空间分布。A kind of event-related potential (Event-related Potentials, ERP) analysis method based on the paired sample T-test of the present invention, first utilizes EEG acquisition equipment to record multi-channel scalp EEG signals under two kinds of stimuli, and performs preliminary preprocessing ; secondly, extract the ERP signals under two kinds of stimuli; carry out the paired sample T test on the ERP signals of each lead under two kinds of external stimuli respectively, and obtain the significant difference period of each lead ERP signal under the two kinds of stimuli; Finally, by calculating the area surrounded by the two ERP signals in the significant difference period, the difference area distribution of all the leads of the whole brain in the significant difference period under the two stimuli can be obtained, and then the ERP signal difference induced by the two stimuli can be displayed by the brain topographic map spatial distribution.
如图1所示，本发明的一种基于配对样本T检验的事件相关电位分析方法，具体包括如下步骤：As shown in Figure 1, a kind of event-related potential analysis method based on paired sample T test of the present invention, specifically comprises the following steps:
1)设计含两种刺激的脑电诱发实验，并利用脑电采集设备记录多个导联的头皮脑电信号，进行初步的预处理；1) Design an EEG-induced experiment containing two stimuli, and use the EEG acquisition equipment to record the scalp EEG signals of multiple leads for preliminary preprocessing;
所述设计含两种刺激的脑电诱发实验，是设计两种视觉、听觉或体感刺激的脑电诱发实验，以两种图片刺激为例，从国际情绪图片库(International Affective PictureSystem，IAPS)中选取积极场景情境图片和消极场景情境图片各10张，图片采用随机出现的方式进行呈现，每张图片呈现时间为1s，图片呈现之前有4s的静息期，用于平复上一张图片所引起的脑电变化。Described design contains the EEG-induced experiment of two kinds of stimuli, is to design the EEG-induced experiment of two kinds of visual, auditory or somatosensory stimuli, take two kinds of picture stimuli as example, from the International Affective Picture System (IAPS) Select 10 pictures of positive scene situations and 10 pictures of negative scene situations. The pictures are presented randomly. The presentation time of each picture is 1s. EEG changes.
所述的利用脑电采集设备记录多个导联的头皮脑电信号，是采用BiosemiActiveTwo脑电采集系统记录15位被试在实验过程中的32导头皮脑电信号，采样率为1024Hz，记录信号总时长为100s。The described scalp EEG signals utilizing EEG acquisition equipment to record a plurality of leads is to adopt BiosemiActiveTwo EEG acquisition system to record 15 subjects' 32-lead scalp EEG signals in the experimental process, the sampling rate is 1024Hz, and the recording signal The total duration is 100s.
所述的初步的预处理，是为去除头皮脑电信号记录过程中的低频漂移、高频干扰以及眼动等其他电生理信号的干扰，对原始脑电信号进行变平均参考、0.5-10Hz带通滤波以及独立成分分析去眼电等的预处理操作。The preliminary preprocessing is to remove low-frequency drift, high-frequency interference, and interference from other electrophysiological signals such as eye movement in the recording process of scalp EEG signals, and perform variable average reference, 0.5-10Hz band Preprocessing operations such as pass filtering and independent component analysis to oculograph.
2)提取两种刺激下的ERP信号；2) Extract the ERP signals under the two stimuli;
所述的提取两种以上刺激下的ERP信号，包括如下过程：The described extraction of ERP signals under two or more stimuli includes the following process:
(1)对初步预处理之后的整段头皮脑电信号进行分割，得到20个时长为4s的静息脑电片段和20个时长为1s的诱发脑电片段，其中，两种刺激对应的诱发脑电片段各10个；(1) Segment the entire scalp EEG signal after preliminary preprocessing to obtain 20 resting EEG segments with a duration of 4 s and 20 evoked EEG segments with a duration of 1 s. 10 EEG fragments each;
(2)选取刺激呈现之前的200ms，即静息期的后200ms为基准脑电，并计算基准脑电的平均幅值，将每个诱发脑电片段减去基准脑电平均幅值，实现去基线操作；(2) Select the 200ms before the stimulus presentation, that is, the last 200ms of the resting period as the reference EEG, and calculate the average amplitude of the reference EEG, and subtract the average amplitude of the reference EEG from each evoked EEG segment to achieve detoxification. Baseline operations;
(3)分别对去基线后的两种诱发脑电片段进行叠加平均，得到每位被试者的每个导联在两种刺激下的ERP信号，分别表示为X＝{Xijk}和Y＝{Yijk}，其中，i＝1,2,……,N1，N1＝15，N1是被试者数目；j＝1,2,……,N2，N2＝32，N2是导联数目，k＝1,2,……,N3，N3＝1024，N3是数据点数。(3) Superimpose and average the two evoked EEG segments after removing the baseline, and obtain the ERP signals of each lead of each subject under the two stimuli, which are respectively expressed as X={X ijk } and Y ={Y ijk }, wherein, i=1,2,...,N 1 , N 1 =15, N 1 is the number of subjects; j=1,2,...,N 2 , N 2 =32, N 2 is the number of leads, k=1, 2, ..., N 3 , N 3 =1024, and N 3 is the number of data points.
由多次重复刺激下的头皮脑电信号叠加平均得到ERP信号的依据是：自发脑电是一种类随机信号，多次叠加可使自发脑电幅值降低；而ERP信号具有明显的锁时特性，多次叠加可使ERP信号幅值增加。The basis for obtaining the ERP signal by superimposing and averaging the scalp EEG signals under repeated stimulation is that the spontaneous EEG is a kind of random signal, and multiple superpositions can reduce the amplitude of the spontaneous EEG; while the ERP signal has obvious time-locking characteristics , multiple superposition can increase the amplitude of ERP signal.
3)对两种刺激下的ERP信号进行配对样本T检验，确定具有显著差异的时段；3) Perform a paired sample T-test on the ERP signals under the two stimuli to determine the time periods with significant differences;
由10次重复刺激下的头皮脑电信号求平均得到的ERP信号中，自发脑电的幅值仍然很大，因此，各个ERP成分并不突出，无法进行ERP成分幅值和潜伏期的提取，也无法在不同种类刺激之间进行对比。由于不同种类刺激下的自发脑电虽然不是完全一致，但也不具有显著的差异性，因此，通过对不同种类刺激下的ERP信号进行T检验，得到的显著差异时段必为两真实ERP信号具有显著差异的时段，进一步实现了ERP信号与自发脑电的剥离。In the ERP signal obtained by averaging the scalp EEG signals under 10 repeated stimulations, the amplitude of the spontaneous EEG is still very large. Therefore, each ERP component is not prominent, and it is impossible to extract the amplitude and latency of the ERP component. It is not possible to make comparisons between different kinds of stimuli. Since the spontaneous EEG under different types of stimuli is not exactly the same, but there is no significant difference, therefore, by performing T-test on the ERP signals under different types of stimuli, the significant difference period obtained must be the period between the two real ERP signals. Significantly different time periods further realize the stripping of ERP signals and spontaneous EEG.
所述的对两种刺激下的ERP信号进行配对样本T检验，是对每个导联中的每个数据点对应的ERP序列进行配对样本T检验，由于是同一批被试在同一个实验中接受的两种刺激，所以选用配对样本T检验，显著性水平设置为0.05。对于第j个导联第k个数据点，首先建立一个新变量Z＝{Zijk}，Zijk＝Xijk-Yijk，i＝1,2,……,N1，计算新变量的均值和方差构造统计量检验tjk是否服从自由度为N1-1的T分布，并计算出对应的显著程度Pjk，若Pjk<0.05，则序列和序列具有显著性差异，即两种刺激下的第j个导联第k个数据点具有显著性差异，否则，两种刺激下的第j个导联第k个数据点不具有显著性差异。The paired sample T test for the ERP signals under the two stimuli is to perform a paired sample T test for the ERP sequence corresponding to each data point in each lead, because the same batch of subjects in the same experiment The two stimuli received, so the paired sample T test was selected, and the significance level was set at 0.05. For the kth data point of the jth lead, first establish a new variable Z={Z ijk }, Z ijk =X ijk -Y ijk , i=1,2,...,N 1 , and calculate the mean value of the new variable and variance construction statistics Test whether t jk obeys the T distribution with N 1 -1 degrees of freedom, and calculate the corresponding significance level P jk , if P jk <0.05, the sequence and sequence There is a significant difference, that is, the kth data point of the jth lead under the two stimuli has a significant difference, otherwise, the kth data point of the jth lead under the two stimuli does not have a significant difference.
所述的确定具有显著差异的时段，是在已确定两种刺激下的第j个导联第k个数据点是否具有显著性差异的基础上进行的，包含如下过程：The period of determining a significant difference is based on determining whether the kth data point of the jth lead under the two stimuli has a significant difference, including the following process:
(1)对于第j个导联的N3个数据点，若存在10个以上的连续数据点k，使得序列和序列具有显著性差异，那么这些连续的数据点k对应的时段就是第j个导联在两种刺激下的ERP信号具有显著差异的时段，若不存在10个以上的连续数据点k，使得序列和序列具有显著性差异，则第j个导联在两种刺激下的ERP信号不具有显著差异时段；(1) For the N 3 data points of the jth lead, if there are more than 10 continuous data points k, such that the sequence and sequence If there is a significant difference, then the period corresponding to these continuous data points k is the period when the ERP signal of the jth lead under the two stimuli has a significant difference. If there are no more than 10 continuous data points k, the sequence and sequence There is a significant difference, then the ERP signal of the jth lead under the two stimuli does not have a significant difference period;
(2)分别对每个导联的N3个数据点进行配对样本T检验，每个导联可能含有多个显著差异时段，也可能不含有显著差异时段。根据所有导联的显著差异时段分布，选择一个以上相对大的时段，使得尽可能包含多数导联的显著差异时段，所选的显著差异时段对应的数据点集标记为其中，r＝1,2,…,m，m为所选的显著差异时段数，可选范围为{1,2,…,100}，nr分别是每个显著差异时段所对应的数据点数，取值均大于10。(2) Paired-sample T-tests were performed on the N 3 data points of each lead, and each lead may contain multiple periods of significant difference, or may not contain significant periods of difference. According to the significant difference period distribution of all leads, select more than one relatively large period, so that the significant difference period of most leads can be included as much as possible, and the data point set corresponding to the selected significant difference period is marked as Among them, r=1,2,...,m, m is the number of significant difference periods selected, the optional range is {1,2,...,100}, n r is the number of data points corresponding to each significant difference period , all values are greater than 10.
4)计算两种刺激下的ERP信号在显著差异时段内的差异面积，并绘制脑地形图，确定差异脑区。4) Calculate the difference area of the ERP signal under the two stimuli within a significant difference period, and draw a brain topographic map to determine the difference brain area.
所述的计算两种刺激下的ERP信号在显著差异时段内的差异面积，是对每位被试者的每个导联均计算两种刺激下的ERP信号在所选显著差异时段内的差异面积，其中，在显著差异时段内的两种刺激下的ERP信号的差异面积为并对所有被试者的数据进行叠加平均，得到第j个导联在所选显著差异时段内两种刺激下的ERP信号差异面积 The calculation of the difference area of the ERP signal under the two stimuli within the significant difference period is to calculate the difference of the ERP signal under the two stimuli within the selected significant difference period for each lead of each subject area, where, during the period of significant difference The difference area of the ERP signal under the two stimuli within is And the data of all subjects were superimposed and averaged to obtain the ERP signal difference area of the jth lead under the two stimuli in the selected significant difference period
所述的绘制脑地形图、确定差异脑区，是对于每一个显著差异时段，均根据所有导联的两种刺激下的ERP信号差异面积绘制脑地形图，进而分析两种刺激所诱发ERP信号差异的空间分布状况，得到在显著差异时段内的主要激活脑区分布。The drawing of the brain topography map and the determination of the difference brain region are to draw the brain topography map according to the ERP signal difference area under the two stimuli of all leads for each significant difference period, and then analyze the ERP signals induced by the two stimuli According to the spatial distribution of the difference, the distribution of the main activated brain regions in the significant difference period was obtained.
脑电地形图是一种集中表达大脑电生理信息的图形技术，通常将多个导联的单个特征用不同颜色映射其值大小而得到的头部平面彩色图形(或灰度差图像)，能比较直观地反应大脑神经活动的波幅和分布。EEG topographic map is a graphics technology that expresses the electrophysiological information of the brain in a concentrated manner. Usually, the single features of multiple leads are mapped with different colors to obtain the head plane color graphics (or grayscale difference images). It can more intuitively reflect the amplitude and distribution of brain neural activity.
尽管上面结合附图对本发明的优选实施例进行了描述，但是本发明并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，并不是限制性的。Although the preferred embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments, which are only illustrative and not restrictive.
本领域的普通技术人员在本发明的启示下，在不脱离本发明宗旨和权利要求所保护的范围情况下，还可以作出很多形式，这些均属于本发明的保护范围之内。Under the enlightenment of the present invention, those skilled in the art can also make many forms without departing from the gist of the present invention and the scope of protection of the claims, and these all belong to the protection scope of the present invention.
Claims (3)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410836452.3A CN104545900B (en) | 2014-12-29 | 2014-12-29 | Event related potential analyzing method based on paired sample T test |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410836452.3A CN104545900B (en) | 2014-12-29 | 2014-12-29 | Event related potential analyzing method based on paired sample T test |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104545900A CN104545900A (en) | 2015-04-29 |
| CN104545900B true CN104545900B (en) | 2017-02-22 |
Family
ID=53063756
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410836452.3A Expired - Fee Related CN104545900B (en) | 2014-12-29 | 2014-12-29 | Event related potential analyzing method based on paired sample T test |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104545900B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105653039B (en) * | 2016-01-20 | 2018-12-18 | 同济大学 | A kind of hand motion automatic straightening recognition methods based on EEG signals detection |
| CN108784692A (en) * | 2018-05-11 | 2018-11-13 | 上海大学 | A kind of Feeling control training system and method based on individual brain electricity difference |
| CN110327043B (en) * | 2019-08-05 | 2022-04-01 | 哈尔滨工业大学 | Event-related potential waveform map solving method based on sparse modeling |
| CN111008610B (en) * | 2019-12-16 | 2024-03-22 | 哈尔滨工业大学 | Information-related brain potential evoked experiment method |
| CN111096744A (en) * | 2020-01-21 | 2020-05-05 | 苏州小蓝医疗科技有限公司 | Novel electroencephalogram signal acquisition implementation device and method |
| CN111281382B (en) * | 2020-03-04 | 2023-08-18 | 徐州市健康研究院有限公司 | Feature extraction and classification method based on electroencephalogram signals |
| CN113520310A (en) * | 2021-03-15 | 2021-10-22 | 天津大学 | Electroencephalogram ERP-based touch information processing method |
| CN113419626B (en) * | 2021-06-17 | 2023-03-28 | 深圳大学 | Method and device for analyzing steady-state cognitive response based on sound stimulation sequence |
| CN113743494B (en) * | 2021-08-31 | 2025-01-24 | 清华大学 | Comparison method and system for event-related potential signals |
| CN114246594B (en) * | 2021-12-17 | 2024-04-09 | 天津大学 | Electroencephalogram signal processing method, background electroencephalogram prediction model training method and device |
| CN114081512A (en) * | 2021-12-20 | 2022-02-25 | 天津大学 | A method for assessing the extent to which transcranial direct current stimulation affects auditory processing in the brain |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5331969A (en) * | 1985-07-30 | 1994-07-26 | Swinburne Limited | Equipment for testing or measuring brain activity |
| US5709214A (en) * | 1996-05-02 | 1998-01-20 | Enhanced Cardiology, Inc. | PD2i electrophysiological analyzer |
| CN102778949A (en) * | 2012-06-14 | 2012-11-14 | 天津大学 | Brain-computer interface method based on SSVEP (Steady State Visual Evoked Potential) blocking and P300 bicharacteristics |
| CN103092971A (en) * | 2013-01-24 | 2013-05-08 | 电子科技大学 | Classification method used in brain-computer interfaces |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6996261B2 (en) * | 2001-01-30 | 2006-02-07 | Decharms R Christopher | Methods for physiological monitoring, training, exercise and regulation |
| AU2005328369A1 (en) * | 2004-06-14 | 2006-09-08 | Cephos Corp. | Question and control paradigms for detecting deception by measuring brain activity |
-
2014
- 2014-12-29 CN CN201410836452.3A patent/CN104545900B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5331969A (en) * | 1985-07-30 | 1994-07-26 | Swinburne Limited | Equipment for testing or measuring brain activity |
| US5709214A (en) * | 1996-05-02 | 1998-01-20 | Enhanced Cardiology, Inc. | PD2i electrophysiological analyzer |
| CN102778949A (en) * | 2012-06-14 | 2012-11-14 | 天津大学 | Brain-computer interface method based on SSVEP (Steady State Visual Evoked Potential) blocking and P300 bicharacteristics |
| CN103092971A (en) * | 2013-01-24 | 2013-05-08 | 电子科技大学 | Classification method used in brain-computer interfaces |
Non-Patent Citations (1)
| Title |
|---|
| 情绪词汇加工的心理生理和病理机制 ——事件相关电位时空模式研究;陈湛愔;《医药卫生科技辑》;20090515(第2009/05期);正文第58页第1行到第93页最后一行及图2-11-2-15 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104545900A (en) | 2015-04-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104545900B (en) | Event related potential analyzing method based on paired sample T test | |
| Zou et al. | Automatic identification of artifact-related independent components for artifact removal in EEG recordings | |
| CN108366730B (en) | Method and system for preprocessing EEG signals for cognitive load measurement | |
| CN104182042B (en) | A kind of brain-machine interface method of multi-modal signal | |
| Lin et al. | Real‐Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering | |
| CN106569604B (en) | Audiovisual bimodal semantic matches and semantic mismatch collaboration stimulation brain-machine interface method | |
| Ko et al. | Single channel wireless EEG device for real-time fatigue level detection | |
| JP6856860B2 (en) | Concentration ratio evaluation device, concentration evaluation method, and program | |
| Hammer et al. | Predominance of movement speed over direction in neuronal population signals of motor cortex: intracranial EEG data and a simple explanatory model | |
| US20210196182A1 (en) | Quantifying motor function using eeg signals | |
| CN106175757B (en) | Behaviour decision making forecasting system based on brain wave | |
| CN110215206A (en) | Stereoscopic display visual fatigue evaluation method, system, device based on EEG signals | |
| KR20150123423A (en) | Method and system for classifying emotion of user using brain-wave | |
| Du et al. | Alpha activity during emotional experience revealed by ERSP | |
| Zou et al. | Automatic EEG artifact removal based on ICA and Hierarchical Clustering | |
| CN116369950A (en) | Target detection method based on electroencephalogram tracing and multi-feature extraction | |
| CN105700687A (en) | Single-trial electroencephalogram P300 component detection method based on folding HDCA algorithm | |
| Gaurav et al. | Characterizing neural activity during video game engagement using eeg sensor based topological dynamics analysis | |
| Aldea et al. | Detecting sensorimotor rhythms from the EEG signals using the independent component analysis and the coefficient of determination | |
| Lei et al. | Common spatial pattern ensemble classifier and its application in brain-computer interface | |
| Risangtuni et al. | Towards online application of wireless EEG-based open platform Brain Computer Interface | |
| Rizi et al. | Spatial and spatio-temporal filtering based on common spatial patterns and Max-SNR for detection of P300 component | |
| Meshriky et al. | An intermixed color paradigm for P300 spellers: A comparison with gray-scale spellers | |
| Dzianok et al. | Detecting attention in Hilbert-transformed EEG brain signals from simple-reaction and choice-reaction cognitive tasks | |
| JP2018057510A (en) | Stress evaluation device and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170222 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |