Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements

TLDR

Artificial intelligence systems lag behind animal brains, underscoring the need for brain‑inspired AI, and this study pioneers the search for neural correlates of muscle activity and kinematics from EEG using such a paradigm. The paper proposes a novel BI‑SNN model for incremental learning of spike sequences. BI‑SNN maps spiking activity from input channels into a high‑dimensional source space, extends the eSPANNet model with the NeuCube architecture, and uses this representation to predict muscle activity and kinematics from EEG during upper‑limb movements. BI‑SNN successfully predicts continuous muscle activity and kinematics from EEG, shows strong population activity correlation and real‑time feasibility, offers quantitative visual feedback, and outperforms conventional black‑box BCIs as a neural decoder.

Abstract

Abstract Compared to the abilities of the animal brain, many Artificial Intelligence systems have limitations which emphasise the need for a Brain-Inspired Artificial Intelligence paradigm. This paper proposes a novel Brain-Inspired Spiking Neural Network (BI-SNN) model for incremental learning of spike sequences. BI-SNN maps spiking activity from input channels into a high dimensional source-space which enhances the evolution of polychronising spiking neural populations. We applied the BI-SNN to predict muscle activity and kinematics from electroencephalography signals during upper limb functional movements. The BI-SNN extends our previously proposed eSPANNet computational model by integrating it with the ‘NeuCube’ brain-inspired SNN architecture. We show that BI-SNN can successfully predict continuous muscle activity and kinematics of upper-limb. The experimental results confirmed that the BI-SNN resulted in strongly correlated population activity and demonstrated the feasibility for real-time prediction. In contrast to the majority of Brain–Computer Interfaces (BCIs) that constitute a ‘black box’, BI-SNN provide quantitative and visual feedback about the related brain activity. This study is one of the first attempts to examine the feasibility of finding neural correlates of muscle activity and kinematics from electroencephalography using a brain-inspired computational paradigm. The findings suggest that BI-SNN is a better neural decoder for non-invasive BCI.