Brain-Computer Interface using temporal-spectral features and neural network classifier

We show that by extracting temporal and spectral features from EEG signal 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 5.96% compared to state-of-the-art benchmark algorithms. Using two popular public datasets, our algorithm reaches 91.73% accuracy (compared to an average benchmark of 81.10%) on the first dataset, and 88.78% (average benchmark: 82.76%) 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.