AI Use in EEG Data Processing

A survey for researchers who use, evaluate, or are considering artificial intelligence tools while processing and analyzing EEG data.

About this study

This survey explores how researchers in psychology, neuroscience, and cognitive science process and analyze EEG data, and how they use AI tools in that work.

This effort is co-led by Yu-Fang Yang, PhD (Freie Universität Berlin) and Arnaud Delorme, PhD (UC San Diego).

Estimated completion time: about 15 minutes.

Survey responses are anonymized, stored without direct identifiers, and used in aggregate. The separate drawing offers three $500 prizes, totaling $1,500. Survey participation or completion is not required to enter, but entry requires an institutional email address. Drawing emails are stored separately and are not tied to survey data.

EEG processing refers to the steps that convert raw electroencephalographic recordings into analyzable signals and measures, including importing, montaging, filtering, bad-channel handling, re-referencing, artifact correction, epoching, baseline correction, time-frequency decomposition, source estimation, quality control, and statistical procedures.
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Reviewed by the UC San Diego Office of IRB Administration and determined to be exempt, Protocol #815237.