An EEG artifact identification embedded system using ICA and multi-instance learning

Abstract

Electroencephalogram (EEG) data is used for a variety of purposes, including brain-computer interfaces, disease diagnosis, and determining cognitive states. Yet EEG signals are susceptible to noise from many sources, such as muscle and eye movements, and motion of electrodes and cables. Traditional approaches to this problem involve supervised training to identify signal components corresponding to noise so that they can be removed. However these approaches are artifact specific. In this paper, we present a novel software-hardware system that uses a weak supervisory signal to indicate that some noise is occurring, but not what the source of the noise is or how it is manifested in the EEG signal. The EEG data is decomposed into independent components using ICA, and these components form bags that are labeled and classified by a multi-instance learning algorithm that can identify the noise components for removal to reconstruct a clean EEG signal. We also performed extensive hyperparameter optimization for the model with the goal of improving accuracy without increasing execution time. This resulted the execution time to be reduced from 282 s to 8.8 s when running the model on an embedded ARM CPU processor at 1.6 GHz clock frequency. In this paper, we present the overall system which includes ICA, SAX and MIL, along with preliminary results for software and hardware implementation when using real EEG data from 64 electrodes. The proposed system consumes 909 mW power during processing above a baseline of 2.32 W idle, while achieving 91.2% artifact identification accuracy.