EEG signals, in addition to motion artifacts, suffer from other forms of artifacts among which ocular, muscular, and cardiac artifacts are prominent. Autoencoders (AEs) based on fully connected layers were developed by Ghosh et al. [38] and Yang et al. [39] to eliminate ocular artifacts from EEG signals. Leite et al. [40], Zhang et al. [41], and Sun et al. [42] introduced deep convolutional neural network (DCNN)-based models that can extract spatio-temporal information and are hence more resilient than typical fully connected neural networks. In [40], a deep convolutional autoencoder (DCAE) was developed to reduce eye blink and jaw clenching aberrations from EEG data. To reduce muscular distortions from EEG data, authors in [41] developed a DCNN that progressively increases its width. Sun et al. [42] reported a residual-connection-based DCNN for reducing ocular, muscular, and cardiac abnormalities from noisy EEG data. Recently, authors of [43] proposed EEGANet, a framework based on generative adversarial networks (GANs) for the removal of ocular artifacts from EEG data whereas in [44], the k-means algorithm in combination with the SSA technique was proposed for the reduction of eye blink artifacts. Although a fair share of studies is existent for the removal of ocular, muscle, and cardiac artifacts from EEG recordings to the best of our knowledge, the removal of motion artifacts using deep learning models has not been investigated to date.
Introduction To Neural Networks Using Matlab 6.0 8206 Free 14
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