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Single-trial electroencephalogram P300 component detection method based on folding HDCA algorithm

A component detection and EEG technology, applied in the input/output, computing, computer components and other directions of user/computer interaction, and can solve problems such as latency changes

Active Publication Date: 2016-06-22
THE PLA INFORMATION ENG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The problem to be solved by the present invention is to overcome the problem that in the retrieval of target images based on EEG signals, the subject may watch different target images in different states and cause changes in latency, and provide a single-trial EEG based on foldingHDCA algorithm. P300 composition detection method

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  • Single-trial electroencephalogram P300 component detection method based on folding HDCA algorithm
  • Single-trial electroencephalogram P300 component detection method based on folding HDCA algorithm
  • Single-trial electroencephalogram P300 component detection method based on folding HDCA algorithm

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Embodiment 1

[0025] Embodiment one, see Figure 1~2 As shown, a single-trial EEG P300 component detection method based on the foldingHDCA algorithm includes the following steps:

[0026] Step 1. While the subject watched the rapid sequence visually presenting the RSVP image sequence, the subject's EEG signal was collected through the EEG signal acquisition device;

[0027] Step 2. Divide the EEG signals corresponding to all images into several time windows, take a time window and the multi-lead signals in the first f time windows to form a new set of lead signals, and use Fisher linear discrimination The device gets the spatial weight w=[w k w k-1 …w k-f ] T , which represents the weight of all lead signals between the kth window and the (k-f)th window, multiplying the original multi-lead signal by the spatial weight w to obtain the dimensionality-reduced one-dimensional in the kth time window signal, namely Among them, N represents the number of sampling points in each time window,...

Embodiment 2

[0031] Embodiment two, see Figure 1-5 As shown, a single-trial EEG P300 component detection method based on the foldingHDCA algorithm includes the following steps:

[0032]Step 1. While the subject watches the rapid sequence visual presentation RSVP image sequence, the subject's EEG signal is collected through the EEG signal acquisition device, wherein the EEG signal acquisition device is an EEG instrument, which collects the subject's EEG signal when watching the picture. Electrical signal, the sampling frequency is 256Hz, and the RSVP image sequence presentation speed is 5 images per second;

[0033] Step 2. Divide the EEG signals corresponding to all images into several time windows, take a time window and the multi-lead signals in the first f time windows to form a new set of lead signals, and use Fisher linear discrimination The device gets the spatial weight w=[w k w k-1 …w k-f ] T , which represents the weight of all lead signals between the kth window and the (k-...

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Abstract

The invention relates to a single-trial electroencephalogram P300 component detection method based on the folding HDCA algorithm. A testee is made to watch a series of RSVP image sequences, the electroencephalogram signals of the testee are acquired, the electroencephalogram signals corresponding to all images are divided into a plurality of time windows, multi-lead signals in one time window and f time windows before the time winder form a group of new lead signals, space weight computing and time weigh computing are conducted to obtain an interest score, the interest score is compared with a set threshold value, a target image is judged, and a target image result is output. By correlating the electroencephalogram signals of the testee at the current moment with electroencephalogram signals at previous moments, the dimension of multi-lead electroencephalogram signals is reduced to one, the influences caused by the change of the incubation period and peak value of the P300 component along with the physiological status of the testee, target probability and target meaning in actual use are effectively reduced, the electroencephalogram P300 component is effectively extracted, and then the target image is determined.

Description

technical field [0001] The invention relates to the technical field of EEG signal detection, in particular to a single-trial EEG P300 component detection method based on a folding HDCA algorithm. Background technique [0002] Electroencephalogram (EEG) is the external manifestation of brain activity. As the most important nerve center of the human body, the brain firstly maintains the normal physiological activities of the human body, and secondly assists people to complete various conscious activities. Different brain activities appear as EEG signals with different characteristics. Studies have shown that time-domain and frequency-domain analysis of these EEG patterns is helpful for reverse analysis of human consciousness activities, which provides a theoretical basis for the application of EEG signals. Brain-Computer Interface technology (BCI, Brain-Computer Interface) aims to break the limitation that people must communicate with the outside world through organs, and rea...

Claims

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Application Information

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IPC IPC(8): G06F3/01G06F17/30A61B5/0476A61B5/04
CPCA61B5/316A61B5/369G06F3/015G06F16/50
Inventor 闫镔童莉曾颖林志敏卜海兵高辉梁宁宁王晓娟潘菲
Owner THE PLA INFORMATION ENG UNIV
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