Removing Electroencephalographic Artifacts : Comparison between Ica and Pca
نویسندگان
چکیده
Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals , and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records based on an extended version of an Independent Component Analysis (ICA) algorithm 2, 12] for performing blind source separation on linear mixtures of independent source signals. Our results show that ICA can eeectively separate and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably to those obtained using Principal Component Analysis.
منابع مشابه
Removing electroencephalographic artifacts by blind source separation.
Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time ...
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Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic ~EEG! interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time ...
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