Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics

Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a nov...

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Bibliographic Details
Main Authors:	Georgi Rusev, Svetlozar Yordanov, Simona Nedelcheva, Alexander Banderov, Fabien Sauter-Starace, Petia Koprinkova-Hristova, Nikola Kasabov
Format:	Article
Language:	English
Published:	MDPI AG 2025-03-01
Series:	Biomimetics
Subjects:	brain-machine interfaces motor control decoder spiking neural networks neuromorphic systems ECoG personalized neuro-prosthetics
Online Access:	https://www.mdpi.com/2313-7673/10/3/183
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Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics

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