  EEG signals are complex and low-frequency signals. Therefore, they are easily
influenced by external factors. EEG artifact removal is crucial in neuroscience
because artifacts have a significant impact on the results of EEG analysis. The
removal of ocular artifacts is the most challenging among these artifacts. In
this study, a novel ocular artifact removal method is presented by developing
bidirectional long-short term memory (BiLSTM)-based deep learning (DL) models.
We created a benchmarking dataset to train and test proposed DL models by
combining the EEGdenoiseNet and DEAP datasets. We also augmented the data by
contaminating ground-truth clean EEG signals with EOG at various SNR levels.
The BiLSTM network is then fed to features extracted from augmented signals
using highly-localized time-frequency (TF) coefficients obtained by wavelet
synchrosqueezed transform (WSST). We also compare the WSST-based DL model
results with traditional TF analysis (TFA) methods namely short-time Fourier
transformation (STFT) and continuous wavelet transform (CWT) as well as
augmented raw signals. The best average MSE value of 0.3066 was obtained by the
first time-proposed BiLSTM-based WSST-Net model. Our results demonstrated the
WSST-Net model significantly improves artifact removal performance compared to
traditional TF and raw signal methods. Also, the proposed EOG removal approach
reveals that it outperforms many conventional and DL-based ocular artifact
removal methods in the literature.
