Video Category Classification Using Wireless EEG

In this paper, we present a novel idea where we analyzed EEG signals to classify what type of video a person is watching which we believe is the first step of a BCI based video recommender system. For this, we setup an experiment where 13 subjects were shown three different types of videos. To be able to classify each of these videos from the EEG data of the subjects with a very good classification accuracy, we carried out experiments with several state-of-the-art algorithms for each of the submodules (pre-processing, feature extraction, feature selection and classification) of the Signal Processing module of a BCI system in order to find out what combination of algorithms best predicts what type of video a person is watching. We found, the best results (80.0% with 32.32 ms average total execution time per subject) are obtained when data of channel AF8 are used (i.e. data recorded from the electrode located at the right frontal lobe of the brain). The combination of algorithms that achieved this highest average accuracy of 80.0% are FIR Least Squares, Welch Spectrum, Principal Component Analysis and Adaboost for the submodules pre-processing, feature extraction, feature selection and classification respectively.