Comput Methods Programs Biomed - Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.

Tópicos

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Resumo

Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested.

Resumo Limpo

motor imageri mi task classif provid import basi design braincomput interfac bci system mi task reliabl distinguish identifi typic pattern electroencephalographi eeg data motor disabl peopl communic devic compos sequenc mental state earlier studi develop crosscorrel base logist regress cclr algorithm classif mi task bci applic perform satisfactori studi develop modifi version cclr algorithm explor suitabl featur set can improv perform modifi cclr algorithm use c electrod channel intern system refer channel crosscorrel cc techniqu appli three divers featur set separ input logist regress lr classifi present algorithm investig featur set best character distribut mi task base eeg data studi also provid insight select refer channel cc techniqu eeg signal consid anatom structur human brain propos algorithm compar eight recent report wellknown method includ bci iii winner algorithm find studi indic modifi cclr algorithm potenti improv identif perform mi task bci system result demonstr propos techniqu provid classif improv exist method test

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