Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.

Autor: Haghi B; Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA. benyamin.a.haghi@caltech.edu., Aflalo T; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. taflalo@caltech.edu., Kellis S; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.; Neurostimulation Center and Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.; Blackrock Microsystems, Salt Lake City, UT, USA., Guan C; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA., Gamez de Leon JA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA., Huang AY; Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA., Pouratian N; UT Southern Medical Center, Dallas, TX, USA.; UCLA Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA., Andersen RA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA., Emami A; Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA. azita@caltech.edu.; Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA. azita@caltech.edu.
Jazyk: angličtina
Zdroj: Nature biomedical engineering [Nat Biomed Eng] 2024 Dec 06. Date of Electronic Publication: 2024 Dec 06.
DOI: 10.1038/s41551-024-01297-1
Abstrakt: To infer intent, brain-computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters. In all three participants, the neural network led to offline and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We also show that the trained neural network can be used without modification for new datasets, brain areas and participants.
Competing Interests: Competing interests: The authors declare no competing interests.
(© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)
Databáze: MEDLINE
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