Time Majority Voting, a PC-based EEG Classifier for Non-expert Users
This work addresses data collection issues in Brain-Computer Interfaces for non-expert users, though it appears incremental as it builds on existing machine learning approaches.
The paper tackled the problem of limited EEG data for cognitive task prediction by developing Time Majority Voting (TMV), a PC-based classifier that outperformed cutting-edge algorithms in experiments.
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.