Comput Math Methods Med - Local temporal correlation common spatial patterns for single trial EEG classification during motor imagery.

Tópicos

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Resumo

Common spatial pattern (CSP) is one of the most popular and effective feature extraction methods for motor imagery-based brain-computer interface (BCI), but the inherent drawback of CSP is that the estimation of the covariance matrices is sensitive to noise. In this work, local temporal correlation (LTC) information was introduced to further improve the covariance matrices estimation (LTCCSP). Compared to the Euclidean distance used in a previous CSP variant named local temporal CSP (LTCSP), the correlation may be a more reasonable metric to measure the similarity of activated spatial patterns existing in motor imagery period. Numerical comparisons among CSP, LTCSP, and LTCCSP were quantitatively conducted on the simulated datasets by adding outliers to Dataset IVa of BCI Competition III and Dataset IIa of BCI Competition IV, respectively. Results showed that LTCCSP achieves the highest average classification accuracies in all the outliers occurrence frequencies. The application of the three methods to the EEG dataset recorded in our laboratory also demonstrated that LTCCSP achieves the highest average accuracy. The above results consistently indicate that LTCCSP would be a promising method for practical motor imagery BCI application.

Resumo Limpo

common spatial pattern csp one popular effect featur extract method motor imagerybas braincomput interfac bci inher drawback csp estim covari matric sensit nois work local tempor correl ltc inform introduc improv covari matric estim ltccsp compar euclidean distanc use previous csp variant name local tempor csp ltcsp correl may reason metric measur similar activ spatial pattern exist motor imageri period numer comparison among csp ltcsp ltccsp quantit conduct simul dataset ad outlier dataset iva bci competit iii dataset iia bci competit iv respect result show ltccsp achiev highest averag classif accuraci outlier occurr frequenc applic three method eeg dataset record laboratori also demonstr ltccsp achiev highest averag accuraci result consist indic ltccsp promis method practic motor imageri bci applic

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