Artifact signals from eye movements, heart beat and muscle activity contaminate magnetoencephalographic (MEG) signals generated from the neural activities inside the brain. Rejection of contaminated trials not only causes data loss, but can also significantly increase the experimental time or even prevent the analysis of highly contaminated or noisy data. We combined the use of independent component analysis (ICA) and clustering methods to isolate the artifacts from MEG signals. Threshold-based clustering analyses based on the topographic pattern, statistical aspects and power spectral patterns of independent components (ICs) successfully identified ICs related to certain types of artifacts. Unsupervised neural network based on the Adaptive Resonance Theory (ART) also categorized the artifact ICs, albeit with lower accuracy. Performance of the identification methods were evaluated with measurements of underestimation and overestimation of the target artifactual ICs. The combination of threshold-based clustering and ART-2 neural network categorization methods demonstrated the best identification performance. Comparison between contaminated and artifact-cleaned MEG signal waveforms showed the efficiency of the proposed methods of artifacts rejection. The analysis of the artifact components suggested the possibility of automatic artifact removal based on general templates.
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