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 EEG denoising literature. ICA-based artifact removal estimates the component activity by unmixing the EEG data in the channel domain. Through manual or automatic identification, one can exclude the artifact components and then reconstruct the EEG data through back projection based on non-artifact components (Jung et al., 2000a) . The fast growth of deep learning methods has drawn state-of-the-art performances in a variety of machine learning problems (LeCun et al., 2015) . Lately, deep-learning-based EEG reconstruction has drawn much attention (Leite et al., 2018; Sun et al., 2020; Lopes et al., 2021; Lee et al., 2020; Chuang et al., 2022) . Although these methods can effectively remove artifacts from artificial synthetic signals, their performance in reconstructing real EEG data has not yet been validated in terms of decoding labeled EEG data. Meanwhile, the model design of existing deep-learning-based techniques for EEG reconstruction rarely takes the characteristics of EEG into account. In this work, we propose CLEEGN, a ConvoLutional neural network for EEG reconstructioN. CLEEGN is capable of subject-independent EEG construction without any training/calibration for a new subject. The contributions of this work are three-fold: • a light-weight autoencoder CNN, CLEEGN, with a subject-independent framework that facilitates plug-and-play EEG reconstruction. • CLEEGN outperforms leading online/offline methods in providing reconstructed EEG data with the best decoding performance for BCI datasets. • with a novel model design dedicated to EEG reconstruction, CLEEGN characterizes patterns of EEG interpretable and provides neuroscientific insights.

2. RELATED WORK

Current processing techniques for EEG artifact removal are highly varied based on the context where the algorithm may apply. Earlier attempts of EEG denoising assumed that the EEG signals and artifacts appear in different frequency ranges. Based on the assumption, some significant artifacts can be eliminated by the linear filtering method during the online stage (Seifzadeh et al., 2014) 



. Despite the advantages of low computational time, linear filtering hardly removes artifacts that distribute in an overlapped frequency range of EEG signals. Another approach, adaptive filtering(Schlögl et al.,  2007), estimates artifact signals through additional EOG, EMG, ECG channels and removes these noisy signals from the recording signals by regression. Nevertheless, this approach requires additional auxiliary electrodes and raises the cost and inconvenience in practical applications. The blind source separation (BSS) method in EEG denoising was developed by assuming that the recording EEG signals are linear combinations of the signals from noise sources and the brain neurons. One of the most well-known BSS method is independent component analysis (ICA)(Jung et al., 2000a;b), which is able to separate EEG signals into independent components (ICs)(Makeig et al., 1995).Traditionally, the artifact components extracted by ICA are determined and removed through manual inspection. Recently developed ICLabel can label the ICs provenance into seven different categories: brain, eye, heart, muscle, line noise, channel noise, and other(Pion-Tonachini et al., 2019). Artifact subspace reconstruction (ASR) is another automatic approach, which is based on the principal component analysis (PCA) method (Kothe & Jung, 2016). The ASR method selects relatively noiseless periods from the multi-channel EEG data as reference based on the data distribution. After projecting all EEG data to the principal-component domain, high-variance components projected from the artifacts are detected by a cutoff parameter k. The noiseless signals are reconstructed by preserving the components without carrying artifacts and back-projected to the time domain. The ASR method has been shown capable of improving the quality of ICA decomposition(Chang et al., 2020).Recently, neural network-based methods have been proposed to remove artifacts for EEG data. A variety of network structures have been applied to the framework for removing EEG artifacts and reconstructing clean EEG. A deep convolutional autoencoder(Leite et al., 2018)  can enhance the peak-signal-to-noise ratio compared to the linear filtering method via a common CNN autoencoder structure, which has been widely used on image denoising. Their work shows that it seems practicable to transform the EEG waveform through a CNN structure. Later on, a combined framework integrating Bayesian deep learning and ICA(Lee et al., 2020)  used thresholding of the EEG data distribution to discard ICs classified as ocular artifacts. These methods leverage the flexibility of deep learning model design and achieve improvements in their assessments. Considering the non-

