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A Framework for Evaluating ICA Methods of Artifact Removal from Multichannel EEG
[chapter]
2004
Lecture Notes in Computer Science
We present a method for evaluating ICA separation of artifacts from EEG (electroencephalographic) data. Two algorithms, Infomax and FastICA, were applied to "synthetic data," created by superimposing simulated blinks on a blink-free EEG. To examine sensitivity to different data characteristics, multiple datasets were constructed by varying properties of the simulated blinks. ICA was used to decompose the data, and each source was cross-correlated with a blink template. Different thresholds for
doi:10.1007/978-3-540-30110-3_130
fatcat:p2exega3gzfq5n4bkx7bknszd4