An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding
Published in Scientific reports, 2020
Recommended citation: Nakagome, S., Luu, T. P., He, Y., Ravindran, A. S., & Contreras-Vidal, J. L. (2020). An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding. Scientific reports, 10(1), 1-17.
Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated ofine decoding analysis with diferent models and conditions to assess how they infuence the performance and stability of the decoder. Specifcally, we conducted three computational decoding experiments that investigated decoding accuracy: (1) based on delta band time-domain features, (2) when downsampling data, (3) of diferent frequency band features. In each experiment, eight diferent decoder algorithms were compared including the current stateof-the-art. Diferent tap sizes (sample window sizes) were also evaluated for a real-time applicability assessment. A feature of importance analysis was conducted to ascertain which features were most relevant for decoding; moreover, the stability to perturbations was assessed to quantify the robustness of the methods. Results indicated that generally the Gated Recurrent Unit (GRU) and Quasi Recurrent Neural Network (QRNN) outperformed other methods in terms of decoding accuracy and stability. Previous state-of-the-art Unscented Kalman Filter (UKF) still outperformed other decoders when using smaller tap sizes, with fast convergence in performance, but occurred at a cost to noise vulnerability. Downsampling and the inclusion of other frequency band features yielded overall improvement in performance. The results suggest that neural network-based decoders with downsampling or a wide range of frequency band features could not only improve decoder performance but also robustness with applications for stable use of BCIs