EEG-Pype: An accessible MNE-Python pipeline with graphical user interface for preprocessing and analysis of resting-state electroencephalography data
Fig 2
Independent Component Analysis for the test EEG measurement.
(A) The time series plot shows the EEG signal over the entire measurement duration after ICA decomposition. The window shown here contains (eye) movement artifacts, with eye blinks clearly distinguishable in component ICA000 and muscle activity in multiple other components. The automatically generated IC labels and the algorithm confidence level in percentages, seen in (B), can help to support ICA decision making. The topographical plot (C) confirms that component ICA000 corresponds to ocular artifacts, as can be seen from the field distribution with a maximum over frontal regions. ICA: Independent Component Analysis, s: seconds.