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 indeveloping a BCI is handling the high dimensionality of the data recordedfrom multichannel EEG signals which are highly subject-specific. Designing aportable BCI whilst minimizing EEG channel number is a challenge. To thisend, this paper presents a method to reduce the channel count with the goalof reducing computational complexity whilst maintaining a sufficient level ofaccuracy, by utilising an automatic subject-specific channel selection methodcreated using the Pearson correlation coefficient. This method computes thecorrelation between EEG signals and helps to select highly correlated EEGchannels for a particular subject without compromising classification accuracy (CA). Common spatial patterns (CSP) are used to analyse imaginedleft and right hand movements and the method is evaluated on both BCICompetition III Dataset IIIa and right hand and foot imagined tasks on BCICompetition III Dataset IVa. For both datasets, a minimum number of EEGchannels are identified with an average channel reduction of 65.45% whilstdemonstrating an increase of >5% in CA using channel Cz as a reference.
Bibliographical noteFunding Information:
This work was supported in part by the Northern Ireland Functional Brain Mapping Facility project ( 1303/101154803 ), funded by InvestNI and Ulster University , United Kingdom.
© 2021 Elsevier Ltd
- Brain-computer interface
- common spatial patterns,
- linear discriminant analysis,
- channel selection.
- Channel selection
- Brain–computer interface
- Linear discriminant analysis
- Common spatial patterns