Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study https://www.embs.org/wp-content/uploads/2022/05/students-laptop.jpg 570 428 IEEE EMBS //www.embs.org/wp-content/uploads/2024/06/ieee-embs-tag-tm-logo2x.png
This work introduced for the first time a novel BCI that incorporate both intracortical LFP and scalp EEG (named, LFP-EEG-BCI) for motor intention decoding during neurorehabilitation. Concurrent intracortical and scalp signals were collected from a paraplegic patient undergoing motor imagery (MI) neurorehabilitation training. A common spatial filter approach was adopted for feature extraction and a decision fusion strategy was further introduced to obtain the decoding results. Transfer learning approach was also utilized to reduce the calibration. The proposed novel LFP-EEG-BCI may lead to new directions for developing practical neurorehabilitation systems in clinical applications. read more