An automatic subject specific channel selection method for enhancing motor imagery classification in EEG-BCI using correlation
A motor imagery (MI) based brain–computer interface (BCI) decodes the motor intention from the electroencephalogram (EEG) of a subject and translates this into a control signal. These intentions are hence classified as different cognitive tasks, e.g. left and right hand movements. A challenge in dev...
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| Published in | Biomedical signal processing and control Vol. 68; p. 102574 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.07.2021
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| Abstract | A motor imagery (MI) based brain–computer interface (BCI) decodes the motor intention from the electroencephalogram (EEG) of a subject and translates this into a control signal. These intentions are hence classified as different cognitive tasks, e.g. left and right hand movements. A challenge in developing a BCI is handling the high dimensionality of the data recorded from multichannel EEG signals which are highly subject-specific. Designing a portable BCI whilst minimizing EEG channel number is a challenge. To this end, this paper presents a method to reduce the channel count with the goal of reducing computational complexity whilst maintaining a sufficient level of accuracy, by utilising an automatic subject-specific channel selection method created using the Pearson correlation coefficient. This method computes the correlation between EEG signals and helps to select highly correlated EEG channels for a particular subject without compromising classification accuracy (CA). Common spatial patterns (CSP) are used to analyse imagined left and right hand movements and the method is evaluated on both BCI Competition III Dataset IIIa and right hand and foot imagined tasks on BCI Competition III Dataset IVa. For both datasets, a minimum number of EEG channels are identified with an average channel reduction of 65.45% whilst demonstrating an increase of >5% in CA using channel Cz as a reference. |
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| AbstractList | A motor imagery (MI) based brain–computer interface (BCI) decodes the motor intention from the electroencephalogram (EEG) of a subject and translates this into a control signal. These intentions are hence classified as different cognitive tasks, e.g. left and right hand movements. A challenge in developing a BCI is handling the high dimensionality of the data recorded from multichannel EEG signals which are highly subject-specific. Designing a portable BCI whilst minimizing EEG channel number is a challenge. To this end, this paper presents a method to reduce the channel count with the goal of reducing computational complexity whilst maintaining a sufficient level of accuracy, by utilising an automatic subject-specific channel selection method created using the Pearson correlation coefficient. This method computes the correlation between EEG signals and helps to select highly correlated EEG channels for a particular subject without compromising classification accuracy (CA). Common spatial patterns (CSP) are used to analyse imagined left and right hand movements and the method is evaluated on both BCI Competition III Dataset IIIa and right hand and foot imagined tasks on BCI Competition III Dataset IVa. For both datasets, a minimum number of EEG channels are identified with an average channel reduction of 65.45% whilst demonstrating an increase of >5% in CA using channel Cz as a reference. |
| ArticleNumber | 102574 |
| Author | Pachori, Ram Bilas Prasad, Girijesh McCreadie, Karl Wang, Hui Gaur, Pramod |
| Author_xml | – sequence: 1 givenname: Pramod surname: Gaur fullname: Gaur, Pramod email: pgaur@dubai.bits-pilani.ac.in organization: Department of Computer Science, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates – sequence: 2 givenname: Karl surname: McCreadie fullname: McCreadie, Karl organization: Intelligent Systems Research Centre, Ulster University, United Kingdom – sequence: 3 givenname: Ram Bilas surname: Pachori fullname: Pachori, Ram Bilas organization: Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, India – sequence: 4 givenname: Hui surname: Wang fullname: Wang, Hui organization: School of Computing, Ulster University, United Kingdom – sequence: 5 givenname: Girijesh surname: Prasad fullname: Prasad, Girijesh organization: Intelligent Systems Research Centre, Ulster University, United Kingdom |
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| Keywords | Channel selection Linear discriminant analysis Motor-imagery Brain–computer interface Common spatial patterns |
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| SubjectTerms | Brain–computer interface Channel selection Common spatial patterns Linear discriminant analysis Motor-imagery |
| Title | An automatic subject specific channel selection method for enhancing motor imagery classification in EEG-BCI using correlation |
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