  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 subject-independent 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.
