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

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Abstract

Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer Interfaces (BCI). In contrast to the fields of computer vision and natural language processing, the data amount of these trials is still rather tiny. Developing a PC-based machine learning technique to increase the participation of non-expert end-users could help solve this data collection issue. We created a novel algorithm for machine learning called Time Majority Voting (TMV). In our experiment, TMV performed better than cutting-edge algorithms. It can operate efficiently on personal computers for classification tasks involving the BCI. These interpretable data also assisted end-users and researchers in comprehending EEG tests better.

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APA

Dou, G., Zhou, Z., & Qu, X. (2022). Time Majority Voting, a PC-Based EEG Classifier for Non-expert Users. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13519 LNCS, pp. 415–428). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-17618-0_29

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