[HTML][HTML] Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal
To achieve a highly efficient brain-computer interface (BCI) system regarding emotion
recognition from electroencephalogram (EEG) signal, the most crucial issues are feature
extractions and classifier selection. This work proposes an innovative method that hybridizes
the principal component analysis (PCA) and t-statistics for feature extraction. This work
contributes to successfully implement spatial PCA to reduce signal dimensionality and to
select the suitable features based on the t-statistical inferences among the classes. The …
recognition from electroencephalogram (EEG) signal, the most crucial issues are feature
extractions and classifier selection. This work proposes an innovative method that hybridizes
the principal component analysis (PCA) and t-statistics for feature extraction. This work
contributes to successfully implement spatial PCA to reduce signal dimensionality and to
select the suitable features based on the t-statistical inferences among the classes. The …