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Brain-Computer Interface System Design and Experiment Research Based on Motor Imagery Potential

Author: ChenBinJin
Tutor: WanBaiKun
School: Tianjin University
Course: Biomedical Engineering
Keywords: Brain-Computer Interface (BCI) Motor Imaginary Potential Event-Related Desynchronization (ERD) Time-Frequency Spectrogram (TFS) Power Spectral Density (PSD) Complexity Kc Power Spectral Entropy (PSE) Wavelet Entropy (WE) Mahalanobis Distance Discriminant
CLC: TP334.7
Type: Master's thesis
Year: 2005
Downloads: 213
Quote: 4
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Abstract


Brain-Computer Interface (BCI) is a direct information communication and control channel established between human and computer or other electrical devices and it is a wholly new communication system that does not depend on the brain’s normal output pathways of peripheral nerves and muscles. As a novel information communication and control technology, EEG-Based BCI may provide the paralyzed, especially those“locked-in”but with intact ideation, with an effective communication and control channels with outside world. That’s why BCI is winning more and more attentions.Several kinds of event-related phenomena represent frequency specific changes of the ongoing EEG activity and may consist, in general terms, either of decreases or of increases of power in given frequency bands. This may be considered to be due to a decrease or an increase in synchrony of the underlying neuronal populations, respectively. The former case is called as event-related desynchronization (ERD), and the latter as event-related synchronization (ERS). Motor Imaginary can make specific influence upon the ongoing EEG signals. In terms of this principle, this dissertation presents a simple and easy motor imaginary potential based BCI experimental system. How to recognize this response from dynamic parameters extraction is becoming the key research in this dissertation.In order to find the dynamic parameter which has more characteristic sensitivity, we have analyzed ongoing EEG signals before and after hand imaginary movements by using time-frequency spectrogram (TFS), power spectral density (PSD), complexity Kc and two kinds of information entropy, power spectral entropy (PSE) and wavelet entropy (WE) separately. Compared with event-related desynchronization (ERD) coefficient in mu rhythm, we found that TFS, PSD, Kc, PSE and WE expressed time-locked variances on contra-lateral primary sensorimotor area with obviously low error rate. Furthermore, TFS, PSD, Kc, PSE and WE shown different responses corresponding different hand act imagination. Finally, the test data were analyzed and the analysis results were evaluated by using Mahalanobis distance. Analysis results show that the method used in this paper had a higher positive recognition rate (exceed 80%). Conclusion is that, TFS, PSD, Kc, PSE and WE can be used as recognition components in online BCI system for their good temporal sensitivity and higher

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Electronic digital computer (not a continuous role in computer ) > External device > Interface devices,plug-in
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