Abstract
Abstract
Objective.
Effective control of neural interfaces is limited by poor signal quality. While neural network-based electroencephalography (EEG) denoising methods for electromyogenic (EMG) artifacts have gained traction in recent years, existing models perform suboptimally in settings with high noise. Since neural interfaces rely almost universally on some form of signal processing, uncovering effective algorithmic insights into EEG denoising is crucial for future success.
Approach.
To address the shortcomings of current machine learning (ML)-based denoising algorithms, we present a single-channel signal filtration algorithm driven by a new mixture-of-experts (MoEs) framework. Our algorithm leverages three new statistical insights into the EEG–EMG denoising problem: (1) EMG artifacts can be partitioned into quantifiable types to aid downstream MoE classification, (2) local experts trained on narrower signal-to-noise ratio ranges can achieve performance increases through specialization, and (3) correlation-based objective functions, in conjunction with rescaling algorithms, can enable faster convergence in a neural network-based denoising context.
Main results.
We empirically demonstrate these three insights into EMG artifact removal and use our findings to create a new downstream MoE denoising algorithm consisting of convolutional and recurrent neural networks. We tested all results on a major benchmark dataset (EEGdenoiseNet) collected from 67 subjects. We found that our MoE denoising model achieved competitive overall performance with contemporary ML denoising algorithms and strong lower bound performance in high noise settings. We further validated the framework on the SEED EEG corpus, observing statistically significant downstream classification gains under high-noise contamination.
Significance.
These preliminary results highlight the promise of our MoE framework for enabling advances in EMG artifact removal in EEG, especially in high noise settings. Further experimentation will be necessary to assess our MoE framework on a wider range of test cases and explore its downstream potential to unlock more effective neural interfaces.