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Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

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

Georgi Rusev¹, Svetlozar Yordanov¹, Simona Nedelcheva¹

¹Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria.

Biomimetics (Basel, Switzerland)

|March 26, 2025

Summary

This summary is machine-generated.

This study explores neuromorphic technologies for Brain-Machine Interfaces (BMI), proposing a novel framework using 3D-SNN and ESN for decoding Electro Cortico-Graphic (ECoG) signals to control prosthetics. Initial results are promising for adaptive, low-power BMI systems.

Keywords:

ECoG brain-machine interfaces motor control decoder neuromorphic systems personalized neuro-prosthetics spiking neural networks

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Area of Science:

Neuroscience
Computer Science
Biomedical Engineering

Background:

Current Brain-Machine Interfaces (BMI) face challenges with size, power consumption, and adaptability.
Neuromorphic technologies offer potential solutions to these limitations.

Purpose of the Study:

To investigate the feasibility of using neuromorphic technologies to improve BMI systems.
To propose and evaluate a novel neuromorphic framework for prosthetic control via Electro Cortico-Graphic (ECoG) signal decoding.

Main Methods:

Development of a novel neuromorphic framework integrating a 3D spike timing neural network (3D-SNN) for feature extraction.
Implementation of an online-trainable Echo State Network (ESN) for Motor Control Decoding (MCD).
Software system developed in Python using NEST Simulator, capable of continuous adaptation in real-time (supervised/unsupervised).

Main Results:

The proposed framework was tested on ECoG data from a tetraplegic individual.
Initial simulation results are encouraging, demonstrating the potential of the approach.
Further improvements are needed through hyper-parameter tuning.

Conclusions:

Neuromorphic technologies show promise for developing smaller, less power-consuming, and adaptive BMI systems.
The proposed 3D-SNN and ESN framework is a viable approach for ECoG-based prosthetic control.
Future implementation on neuromorphic hardware is discussed as a next step.