Decoding Motor Behavior Using Deep Learning and Reservoir Computing
By: Tian Lan
Potential Business Impact:
Lets minds control robots by reading brain waves.
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
Similar Papers
Deep Learning Architectures for Code-Modulated Visual Evoked Potentials Detection
Machine Learning (CS)
Lets minds control computers with brain signals.
Towards a Comprehensive Theory of Reservoir Computing
Neural and Evolutionary Computing
Predicts how well computer memory systems work.
Reservoir Network with Structural Plasticity for Human Activity Recognition
Machine Learning (CS)
Lets small computers learn and predict things locally.