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# A neuromorphic neural interface for the bidirectional control of hand prostheses

Corentin Piozin, Hoda Fares, Farshad Moradi

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## To cite this version:

Corentin Piozin, Hoda Fares, Farshad Moradi. A neuromorphic neural interface for the bidirectional control of hand prostheses. 9th International Summer School on Neuroengineering, Jun 2024, Genova, Italy. hal-04692919

HAL Id: hal-04692919

https://hal.science/hal-04692919v1

Submitted on 10 Sep 2024

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Distributed under a Creative Commons Attribution 4.0 International License
# A neuromorphic neural interface for the bidirectional control of hand prostheses

Hoda Fares, Corentin Piozin, Farshad Moradi

Integrated Circuits & Electronics Laboratory, IbrAln Center
Department of Electrical and Computer Engineering
Aarhus University
hfares@ece.au.dk, cpiozin@ece.au.dk, moradi@ece.au.dk

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## Context

The loss of a limb - such as an arm or a leg - is a traumatic event which has implications on multiple aspects: **functional, psychological or social**. To address these challenges, **bidirectional neuroprostheses** are being developed to establish direct communication between the nervous system and external devices to **restore motor and sensory functions**.

In this project, we are developing a **novel bidirectional neural interface** dedicated to the sensorimotor control of hand prostheses for upper-limb amputees which relies on the use of an **intelligent bioinspired wearable chip** to improve functionality and quality of life.

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### 1 Motor Decoding

Movement-related activity is recorded from sensors over the brain (EEG) and over the muscles (EMG). The data is processed inside the **neuromorphic chip** where it will be encoded as spikes and decoded via a **Spiking Neural Network (SNN)**.

### 2 Movement Restoration

The output of the SNN from the **neuromorphic chip** is used to restore movements on a **prosthesis** or a **robotic arm**.

### 3 Sensory Restoration

**Sensors** (pressure, tactile, temperature, etc.) mounted on the prosthesis are transmitting information to the **neuromorphic chip** which will encode sensory data and restore sensations to the user via **stimulation electrodes**.

## Neuromorphic hardware and software

The bidirectional neural interface is designed to be:

- Bioinspired SNN for decoding/encoding

- Wearable and miniaturized

- Low-Power integrated circuit

- Made with soft and flexible materials

- Clinically tested with patients

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## Experimental setup

- Bebionic prosthesis

- EEG systems

- EMG system

- Neurostimulator

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