MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain-Machine Interfaces With EEG Channel Selection

A brain-machine interface (BMI) based on motor imagery (MI) enables the control of devices using brain signals while the subject imagines performing a movement. It plays a key role in prosthesis control and motor rehabilitation. To improve user comfort, preserve data privacy, and reduce the system&#...

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Veröffentlicht in:	IEEE sensors journal 2024-03, Vol.24 (6), p.8835-8847
Hauptverfasser:	Wang, Xiaying, Hersche, Michael, Magno, Michele, Benini, Luca
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Artificial neural networks Batteries Brain Brain modeling Brain–computer interfaces Classification Computational modeling Convolutional neural networks convolutional neural networks (CNNs) edge computing Electroencephalography embedded systems feature reduction Hardware Imagery Inference machine learning Man-machine interfaces motor imagery (MI) Neural networks Power management Prostheses Spatial filtering Task analysis TinyML
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