Comput Math Methods Med - Evaluation of EEG features in decoding individual finger movements from one hand.

Tópicos

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Resumo

With the advancements in modern signal processing techniques, the field of brain-computer interface (BCI) is progressing fast towards noninvasiveness. One challenge still impeding these developments is the limited number of features, especially movement-related features, available to generate control signals for noninvasive BCIs. A few recent studies investigated several movement-related features, such as spectral features in electrocorticography (ECoG) data obtained through a spectral principal component analysis (PCA) and direct use of EEG temporal data, and demonstrated the decoding of individual fingers. The present paper evaluated multiple movement-related features under the same task, that is, discriminating individual fingers from one hand using noninvasive EEG. The present results demonstrate the existence of a broadband feature in EEG to discriminate individual fingers, which has only been identified previously in ECoG. It further shows that multiple spectral features obtained from the spectral PCA yield an average decoding accuracy of 45.2%, which is significantly higher than the guess level (P < 0.05) and other features investigated (P < 0.05), including EEG spectral power changes in alpha and beta bands and EEG temporal data. The decoding of individual fingers using noninvasive EEG is promising to improve number of features for control, which can facilitate the development of noninvasive BCI applications with rich complexity.

Resumo Limpo

advanc modern signal process techniqu field braincomput interfac bci progress fast toward noninvas one challeng still imped develop limit number featur especi movementrel featur avail generat control signal noninvas bcis recent studi investig sever movementrel featur spectral featur electrocorticographi ecog data obtain spectral princip compon analysi pca direct use eeg tempor data demonstr decod individu finger present paper evalu multipl movementrel featur task discrimin individu finger one hand use noninvas eeg present result demonstr exist broadband featur eeg discrimin individu finger identifi previous ecog show multipl spectral featur obtain spectral pca yield averag decod accuraci signific higher guess level p featur investig p includ eeg spectral power chang alpha beta band eeg tempor data decod individu finger use noninvas eeg promis improv number featur control can facilit develop noninvas bci applic rich complex

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