A Novel Procedure for Classification of Early Human Actions from EEG Signals

We introduce a novel procedure that extends the time feasibility for classification of early human actions. Its major characteristic is to use epoch training data from a wider time duration before action onset (i.e., within the intention period) instead of data from localized sliding windows. This is the case of time-specific and selected fixed classifiers. Our approach models human actions from EEG signals and leverages on amplitudes and power frequencies to construct fifteen groups of action vectors, which were subjected to a set of classifiers. Regarding early classification our approach did it earlier than both time-specific and selected fixed classifiers. Moreover, our results reported an increase in classification performance.