Decoding Motor Behavior Using Deep Learning and Reservoir Computing

TL;DR

This paper introduces a hybrid deep learning model combining CNNs and Echo State Networks to improve decoding of motor behavior from EEG signals, outperforming traditional CNN-only approaches.

Contribution

It presents a novel EEG decoding approach integrating reservoir computing with CNNs, enhancing temporal dynamics modeling in brain-machine interfaces.

Findings

Achieved 83.2% within-subject accuracy

Achieved 51.3% leave-one-subject-out accuracy

Outperformed CNN-based baselines

Abstract

We present a novel approach to EEG decoding for non-invasive brain machine interfaces (BMIs), with a focus on motor-behavior classification. While conventional convolutional architectures such as EEGNet and DeepConvNet are effective in capturing local spatial patterns, they are markedly less suited for modeling long-range temporal dependencies and nonlinear dynamics. To address this limitation, we integrate an Echo State Network (ESN), a prominent paradigm in reservoir computing into the decoding pipeline. ESNs construct a high-dimensional, sparsely connected recurrent reservoir that excels at tracking temporal dynamics, thereby complementing the spatial representational power of CNNs. Evaluated on a skateboard-trick EEG dataset preprocessed via the PREP pipeline and implemented in MNE-Python, our ESNNet achieves 83.2% within-subject and 51.3% LOSO accuracies, surpassing widely used CNN-based baselines. Code is available at https://github.com/Yutiankunkun/Motion-Decoding-Using-Biosignals