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Automatic artifact rejection was used, removing epochs for which peak-to-peak amplitudes exceeded 50 μV.
The first step consisted of a combined manual and automatic artifact detection to reject epilepsy-related high-frequency events.
Automatic artifact detection was then performed on artifact-free (by visual inspection) data from the hippocampal channel of each patient using a similar procedure as in ref. 27.
All runs containing transient responses exceeding 65 mV in each recording channel were excluded from the average by an automatic artifact rejection algorithm.
All runs containing transients exceeding 65 μV at each recording channel were excluded from the average by an automatic artifact rejection algorithm.
The automatic artifact rejection was supplemented by visual inspection of every trial to ensure that only trials without artifacts were included in the subsequent analysis.
Similar(28)
Visual and semi-automatic artifact removal were performed [44].
A semi-automatic artifact removal procedure was applied to these epochs.
Semi-automatic artifact rejection was conducted to remove channels with high-frequency noise or interrupted contact with the scalp during individual epochs.
To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information.
Key features include an automatic online artifact reduction method and an evidence accumulation procedure for decision making.
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