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

TL;DR

This paper introduces Time Majority Voting, a novel PC-based EEG classifier that outperforms existing algorithms, facilitating non-expert user participation and better understanding of EEG data in Brain-Computer Interfaces.

Contribution

The paper presents a new machine learning algorithm, Time Majority Voting, optimized for personal computers to improve EEG classification for non-expert users.

Findings

TMV outperforms state-of-the-art algorithms in EEG classification

TMV operates efficiently on personal computers

Interpretable data enhances user and researcher understanding

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.