Automatic ocular artifact removal from EEG data using a hybrid CAE-RLS approach

Traditional methods for removing ocular artifacts(OAs) from electroencephalography(EEG) signals often involve a large number of EEG electrodes or require electrooculogram(EOG) as the reference, these constraints make subjects uncomfortable during the acquisition process and increase the complexity of brain-computer interfaces(BCI). To address these limitations, a method combining a convolutional autoencoder(CAE) and a recursive least squares(RLS) adaptive filter is proposed. The proposed method consists of offline and online stages. In the offline stage, the peak and local mean of the four-channel EOG signals are automatically extracted to obtain the CAE model. Once the model is trained, the EOG channels are no longer needed. In the online stage, by using the CAE model to identify the OAs from a single-channel raw EEG signal, the identified OAs and the given raw EEG signal are used as the reference and input for an RLS adaptive filter. Experiments show that the root mean square error(RMSE) of the CAE-RLS algorithm and independent component analysis(ICA) are 1.253 3 and 1.254 6 respectively, and the power spectral density(PSD) curve for the CAE-RLS is similar to the original EEG signal. These experimental results indicate that by using only a couple of EEG channels, the proposed method can effectively remove OAs without parallel EOG records and accurately reconstruct the EEG signal. In addition, the processing time of the CAE-RLS is shorter than that of ICA, so the CAE-RLS algorithm is very suitable for BCI system.