An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding.

Scientific Reports
Sho NakagomeJose L Contreras-Vidal

Abstract

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 offline decoding analysis with different models and conditions to assess how they influence the performance and stability of the decoder. Specifically, we conducted three computational decoding experiments that investigated decoding accuracy: (1) based on delta band time-domain features, (2) when downsampling data, (3) of different frequency band features. In each experiment, eight different decoder algorithms were compared including the current state-of-the-art. Different 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 (UK...Continue Reading

References

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Citations

Jun 2, 2020·Journal of Neural Engineering·Stefano TortoraFiorenzo Artoni
Dec 30, 2020·Briefings in Bioinformatics·Jesse A Livezey, Joshua I Glaser
Dec 8, 2020·Frontiers in Neurorobotics·Stefano TortoraFiorenzo Artoni
May 18, 2021·Frontiers in Human Neuroscience·Hoai Son Nguyen, Trieu Phat Luu

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Methods Mentioned

BETA
feature extraction

Software Mentioned

fastai
Amazon Web Services ( AWS
TCN
Optuna
CatBoost
QRNN
BrainVision
MATLAB script
ADAM
LightGBM

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