Abstract
The paper presents the results of a comparative study of the artifact subspace re-construction (ASR) method and two other popular methods dedicated to correct EEG artifacts: independent component analysis (ICA) and principal component analysis (PCA). The comparison is based on automatic rejection of EEG signal epochs performed on a dataset of motor imagery data. ANOVA results show a significantly better level of artifact correction for the ASR method. What is more, the ASR method does not cause serious signal loss compared to other methods.
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Acknowledgement
In order to simplify the replication of our results, we have placed a data sets used for analysis in a public repository https://github.com/lareieeg/EEGdata.
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Plechawska-Wojcik, M., Kaczorowska, M., Zapala, D. (2019). The Artifact Subspace Reconstruction (ASR) for EEG Signal Correction. A Comparative Study. In: Świątek, J., Borzemski, L., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018. ISAT 2018. Advances in Intelligent Systems and Computing, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-99996-8_12
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