基于皮尔逊相关性的ERP检测新方法：GW6

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"基于皮尔逊相关性的事件相关电位检测新方法-研究论文" 事件相关电位（ERP）是神经科学研究中的重要工具，特别是在脑机接口（BCI）的应用中，它能揭示大脑对特定刺激的即时反应。ERP是通过记录头皮上的电生理活动（即脑电图，EEG）来获取的，然而，由于正常EEG信号包含大量背景噪声，所以通常需要对多次试验数据进行平均处理，以增强ERP信号并减小噪声的影响。传统的ERP检测方法基于时间同步和相位锁定的波形平均，通过增加平均次数N，可以将噪声降低N的平方根次方。这篇研究论文提出了一种名为GW6的新方法，该方法利用皮尔逊相关性来检测ERP。这种方法的独特之处在于，它不需要传统的波形平均，而是通过计算不同通道间的EEG信号与刺激的相关性来识别ERP。这种方法产生的图形保持了与传统ERP相同的时间分辨率，而且仅显示正峰值，这些峰值代表了与刺激一致的EEG信号相关性的增加。这种方法有助于揭示那些在标准平均方法下可能被掩盖的、非相位锁定的ERP响应成分。这些隐藏的成分可能是由ERP的固有相位潜伏期变化（即抖动）引起的，即使在连续的刺激之间，ERP在所有EEG通道上都显示出相对稳定的相位。 GW6方法对于研究这些隐藏的ERP组件特别有价值，因为它能更好地抵抗EEG伪影，提高了数据的纯净度。此外，这种新方法的实现并不复杂，可以直接用Matlab或其他编程语言快速编写，这使得研究人员能够方便地采用该方法进行自己的分析。通过对皮尔逊相关性的运用，GW6方法提供了一种新的视角来理解和解析ERP数据，这对于深入理解大脑的动态反应机制、开发更高效的BCI系统以及推进神经科学研究有着重要意义。同时，这种方法对于检测那些在常规分析中可能被忽视的微妙神经活动变化，尤其是与认知过程和感知处理相关的变化，具有显著优势。因此，GW6方法的引入为ERP研究领域带来了创新和进步，有望开启新的研究方向和应用。

factor is the standard deviation of a good quality EEG

signal, found experimentally using this specific instru-

ment. This number was calculated as K = 20, and this

normalization step created an epoch with a shape identi-

cal to that of the original EEG signal, but transferred

into a uniform scale, with comparable amplitude for

every epoch. Moreover, this normalization step do not

changes the phase correlation among all the EEG chan-

nels. The entire file fully processed as such was saved as

new file in .CSV format, containing all the information

about the start and the end of each stimulus.

Note that it is also possible to pre-process only time-

locked epochs, for example, 3 s long, corresponding to each

stimulus [pre-stimulus + stimulus (1 s) + post-stimulus],

and in general, this procedure gives non-identical results

although very similar to the previous method based on the

filtering and saving of the entire file.

Another common way to pre-process the data for the

ERP calculation is the exclusion of every epoch with an

amplitude above a fixed threshold, for example, 80 μV.

A disadvantage of this technique is that a large number

of epochs could be discard ed and consequently the aver-

age could be calculated on insufficient data. In our soft-

ware, we also used this procedure to eliminate epochs

with strong artifacts above 100 μV still present after the

digital filtration.

In this paper, we will illustrate a new method which is

useful for detecting ERPs even among particularly noisy

signals and with significant latency variations, known as

“latency jitter”.

Our method, called GW6, is less restrictive regarding

the issue of jitter, and allows ERP detection when the

standard approach, based on the average, fails or gives

unsatisfactory results due to several artifacts. However,

the new method does not reproduce the typical biphasic

waveform of the ERP but rather an always positive wave-

form. For this reason, this new procedure is useful if

used together with the classic te chnique of averaging, ra-

ther as an alternative to the latter.

The new method uses Pearson’s correlation extensively

for all EEG signals recorded by a multichannel EEG

device. By using the method of averaging, it is possible

to work with a single EEG channel too, whereas the

GW6 method works only with a multichannel EEG de-

vice, starting from a minimum of about six channels.

Nevertheless, it is also possible to calculate the ERP for

each channel as in the standard method.

In many papers describing a mathematical method to

analyze something, formulas are usually given, which

must be subsequently translated into a computer-

language, for example C, C++, Visual Basic, Java, Python,

Matlab, or other. This step could be difficult and limit

the release and application of some useful methods. In

this paper, we will describe this new algorithm as a ste p-

by-step procedure and also in the simple and well-

known Matlab programming language, in order to ease

its application (see Additional file 1: Appendix).

We describe the basic idea of this new method in

Figs. 1 and 2.

Let us now consider the Fig. 2 and the double data-

window lasting L (about 270 ms, 34 samples) centered

at point X of the signal. We can calculate the linear

Pearson’s correlation between these two data segments,

and the result will be a number r represented by the vec-

tor R(x), which can be calculated for every point X sim-

ply by progressively moving the windows along by one

sample unit at a time (sliding windows). In general, the

averaged value of R(x) will vary from the pre-stimulus

zone to the stimulus zone because the (auditory or/and

visual) stimulus changes the correlation between the two

EEG signals, which represent the activity of different

parts of the brain. An interval about 270 ms long was se-

lected because it represents the typical amount of time

required for a conscious response corresponding to the

P300 wave, but different intervals could be selected for

fast Evoked potentials, or other types of stimulus.

This change of correlation can appear either as an

increase or a d ecrease with respect to the baseline

(i.e., the zone preceding the stimulus). Let us consider

a real example, based on the Emotiv EPOC®, where

the number of channels is N C = 14, the sampling fre-

quency is 128 samples/s , the stimulus l ength is 1 s ,

Fig. 1 The two upper tracks represent the raw signals of two EEG channels in time-locked epochs, whereas the lower track is the average of a

sufficient number (about 100) of epochs for each channel (ERP is not to scale). The figure shows a positive peak about 300 ms after the stimulus’s

onset (P300 wave). The ERP’s typical duration is about 300–500 ms, depending on the kind of stimulus and band-pass filtering of the signal

Giroldini et al. EURASIP Journal on Bioinformatics and Systems Biology (2016) 2016:11 Page 4 of 13

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