2021/03/09 by Pablo G. Ortega, Tong Zhao, Ortega, Pablo +3
Medicine · Neuroscience · #Advanced MRI Techniques and Applications #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2103.05334
openalex publication_date 2021/03/09 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Non-invasive cortical neural interfaces have only achieved modest performance in cortical decoding of limb movements and their forces, compared to invasive brain-computer interfaces (BCIs). While non-invasive methodologies are safer, cheaper and vastly more accessible technologies, signals suffer from either poor resolution in the space domain (EEG) or the temporal domain (BOLD signal of functional Near Infrared Spectroscopy, fNIRS). The non-invasive BCI decoding of bimanual force generation and the continuous force signal has not been realised before and so we introduce an isometric grip force tracking task to evaluate the decoding. We find that combining EEG and fNIRS using deep neural networks works better than linear models to decode continuous grip force modulations produced by the left and the right hand. Our multi-modal deep learning decoder achieves 55.2 FVAF[%] in force reconstruction and improves the decoding performance by at least 15% over each individual modality. Our results show a way to achieve continuous hand force decoding using cortical signals obtained with non-invasive mobile brain imaging has immediate impact for rehabilitation, restoration and consumer applications.