CLEEGN: A CONVOLUTIONAL NEURAL NETWORK FOR PLUG-AND-PLAY AUTOMATIC EEG RECON-STRUCTION

Abstract

Human electroencephalography (EEG) is a brain monitoring modality that senses cortical neuroelectrophysiological activity in high-temporal resolution. One of the greatest challenges posed in applications of EEG is the unstable signal quality susceptible to inevitable artifacts during recordings. To date, most existing techniques for EEG artifact removal and reconstruction are applicable to offline analysis solely, or require individualized training data to facilitate online reconstruction. We have proposed CLEEGN, a novel convolutional neural network for plug-and-play automatic EEG reconstruction. CLEEGN is based on a subjectindependent pre-trained model using existing data and can operate on a new user without any further calibration. The performance of CLEEGN was validated using multiple evaluations including waveform observation, reconstruction error assessment, and decoding accuracy on well-studied labeled datasets. The results of simulated online validation suggest that, even without any calibration, CLEEGN can largely preserve inherent brain activity and outperforms leading online/offline artifact removal methods in the decoding accuracy of reconstructed EEG data. In addition, visualization of model parameters and latent features exhibit the model behavior and reveal explainable insights related to existing knowledge of neuroscience. We foresee pervasive applications of CLEEGN in prospective works of online plug-and-play EEG decoding and analysis.

1. INTRODUCTION

Since the first record of human electroencephalogram (EEG) performed almost a century ago (in 1924) , EEG has been one of the most widely used non-invasive neural modalities that monitors brain activity in high temporal resolution (Koike et al., 2013; Mehta & Parasuraman, 2013; Sejnowski et al., 2014) . Among a variety of modalities, EEG has extensive use in the clinical assessment of neurological and psychiatric conditions, as well as in the research of neuroscience, cognitive science, psychology, and brain-computer interfacing (BCI). EEG signals measure subtle fluctuations of the electrical field driven by local neuroelectrophysiological activity of a population of neurons in the brain cortex (Cohen, 2017) . While the electrodes are placed on the surface of the scalp, undesired artifacts may introduce interruption in the measurements and distort the signal of interest. Even in a well-controlled laboratory with a well-trained subject who can maximally keep the body still and relaxed, the EEG signals, unfortunately, could be contaminated by inevitable behavioral and physiological artifacts such as eye blinks, reflective muscle movements, ocular activity, cardiac activity, etc (Croft & Barry, 2000; Wallstrom et al., 2004; Romero et al., 2008) . In practice, it is difficult to identify and track the sources of artifacts entirely due to their diversity and non-stationarity. Noise cancellation and artifact removal remain major issues in EEG signal processing and decoding. Currently, numerous methods have been proposed to alleviate the influence of artifacts in EEG signals. Traditional EEG denoising algorithms include filtering, regression, data separation or decomposition (Makeig et al., 1995; Islam et al., 2016; Kothe & Jung, 2016) . According to previous meta-analyses on EEG artifact removal literature (Urigüen & Garcia-Zapirain, 2015; Jiang et al., 2019) , independent component analysis (ICA) is especially popular. It is majorly used in 45% of 1

