AUTHOR=Wang Gan , Cerf Moran 

TITLE=Brain-Computer Interface using neural network and temporal-spectral features

JOURNAL=Frontiers in Neuroinformatics

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2022.952474

DOI=10.3389/fninf.2022.952474

ISSN=1662-5196

ABSTRACT=We show that by extracting temporal and spectral features from EEG signals and, following, using neural network to classify those features, one can significantly improve the performance of Brain-Computer Interfaces (BCIs) in predicting which motor movement was imagined by a subject. Our movement prediction algorithm uses Sequential Backward Selection technique to jointly select the temporal and spectral features, and a radial basis function neural network for the classification. The method shows an average performance increase of 3.50% compared to state-of-the-art benchmark algorithms. Using two popular public datasets, our algorithm reaches 90.08% accuracy (compared to an average benchmark of 79.99%) on the first dataset, and 88.74% (average benchmark: 82.01%) on the second dataset. Given the high variability within- and across-subjects in EEG-based motion decoding, we suggest that using features from multiple modalities along with neural network feature selection and classification protocol is likely to increase BCI performance across various tasks.