FastEMD–CCA algorithm for unsupervised and fast removal of eyeblink artifacts from electroencephalogram. (March 2020) - Egambaram, Ashvaany; Badruddin, Nasreen; Asirvadam, Vijanth S.; Begum, Tahamina; Fauvet, Eric; Stolz, Christophe
Highlights: The algorithm yields very high SNR, denoting higher ratio of neural information preservation. The effectiveness of the evaluated algorithm in preserving the underlying neural information in an EEG signal is proved through CC value that approaches near 1, low RMSE and higher SNR value. The computation time of the algorithm is very low, with an average of 63 ms processing time to remove artifacts from 1 s length of EEG signal with 14 channels (256 samples × 14 EEG channels). The algorithm is a feasible solution for applications requiring online removal of eyeblink artifacts, with typically low distortion to the neural signal. Abstract: Online detection and removal of eye blink (EB) artifacts from electroencephalogram (EEG) would be very useful in medical diagnosis and brain computer interface (BCI). In this work, approaches that combine unsupervised eyeblink artifact detection with empirical mode decomposition (EMD), and canonical correlation analysis (CCA), are proposed to automatically identify eyeblink artifacts and remove them in an online manner. First eyeblink artifact regions are automatically identified and an eyeblink artifact template is extracted via EMD, which incorporates an alternate interpolation technique, the Akima spline interpolation. The removal of eyeblink artifact components relies on the elimination of EEG canonical components obtained through CCA, based on cross correlation with the extracted eyeblink artifact template. The proposed algorithm is evaluated and analyzed with respect to its ability in removing eyeblink artifacts and retaining neural information of the EEG signals. Analysis proved that the proposed algorithm, FastEMD–CCA, is efficacious in eyeblink artifact removal with an average accuracy, sensitivity, specificity and error rate of 97.9%, 97.65%, 99.22% and 2.1% respectively. The algorithm is able to clean and remove eyeblink artifacts from a 14-channel EEG of length 1 s, at an average time of 63 ms. This makes it a feasible solution for applications requiring online removal of eyeblink artifacts.