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Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning.

Yifeng Bu¹, Deborah L Harrington^2,3, Roland R Lee^2,3

¹Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.

Cerebral Cortex (New York, N.Y. : 1991)

|May 14, 2023

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This summary is machine-generated.

This study introduces MEG-RPSnet, a deep learning model using magnetoencephalography (MEG) to decode hand gestures with 85.56% accuracy. This non-invasive brain-computer interface (BCI) shows promise for future applications.

Failed At:

2026-06-19T13:49:10.361244+00:00

Keywords:

Brain–computer interface deep learning gesture decoding magnetoencephalogram support vector machine

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