Deep real-time decoding of bimanual grip force from EEG & fNIRS
File(s)
Accepted version
Author(s)
Ortega San Miguel, Pablo
Zhao, Tong
Faisal, A Aldo
Type
Conference Paper
Abstract
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 away 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.
Date Acceptance
2021-02-01
Publisher
IEEE
Copyright Statement
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Source
10th International IEEE EMBS Conference on Neural Engineering (NER 2021)
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Theory & Methods
Engineering, Biomedical
Neurosciences
Computer Science
Engineering
Neurosciences & Neurology
BRAIN-MACHINE INTERFACE
MOTOR
cs.LG
cs.LG
Publication Status
Accepted
Start Date
2021-05-04
Finish Date
2021-05-06
Coverage Spatial
Virtual