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Using Machine Learning and Deep Learning to predict cognitive tasks fromelectroencephalography (EEG) signals is a rapidly advancing field inBrain-Computer Interfaces (BCI). In contrast to the fields of computer visionand natural language processing, the data amount of these trials is stillrather tiny. Developing a PC-based machine learning technique to increase theparticipation of non-expert end-users could help solve this data collectionissue. We created a novel algorithm for machine learning called Time MajorityVoting (TMV). In our experiment, TMV performed better than cutting-edgealgorithms. It can operate efficiently on personal computers for classificationtasks involving the BCI. These interpretable data also assisted end-users andresearchers in comprehending EEG tests better.

Time Majority Voting, a PC-based EEG Classifier for Non-expert Users