title: Classifying Response Correctness across Different Task Sets: A Machine Learning Approach.
abstract: Erroneous behavior usually elicits a distinct pattern in neural waveforms. In particular, inspection of the concurrent recorded electroencephalograms (EEG) typically reveals a negative potential at fronto-central electrodes shortly following a response error (Ne or ERN) as well as an error-awareness-related positivity (Pe). Seemingly, the brain signal contains information about the occurrence of an error. Assuming a general error evaluation system, the question arises whether this information can be utilized in order to classify behavioral performance within or even across different cognitive tasks. In the present study, a machine learning approach was employed to investigate the outlined issue. Ne as well as Pe were extracted from the single-trial EEG signals of participants conducting a flanker and a mental rotation task and subjected to a machine learning classification scheme (via a support vector machine, SVM). Overall, individual performance in the flanker task was classified more accurately, with accuracy rates of above 85%. Most importantly, it was even feasible to classify responses across both tasks. In particular, an SVM trained on the flanker task could identify erroneous behavior with almost 70% accuracy in the EEG data recorded during the rotation task, and vice versa. Summed up, we replicate that the response-related EEG signal can be used to identify erroneous behavior within a particular task. Going beyond this, it was possible to classify response types across functionally different tasks. Therefore, the outlined methodological approach appears promising with respect to future applications.
authors.affiliation: Leibniz Research Centre for Working Environment and Human Factors-Department of Ergonomics, Dortmund, Germany.
authors.forename: Thorsten
authors.initials: T
authors.lastname: Plewan
authors.affiliation: Leibniz Research Centre for Working Environment and Human Factors-Department of Ergonomics, Dortmund, Germany.
authors.forename: Edmund
authors.initials: E
authors.lastname: Wascher
authors.affiliation: Leibniz Research Centre for Working Environment and Human Factors-Department of Ergonomics, Dortmund, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Falkenstein
authors.affiliation: German Sport University Cologne-Institute of Psychology-Department of Performance Psychology, Cologne, Germany.
authors.forename: Sven
authors.initials: S
authors.lastname: Hoffmann
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D003071:Cognition; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D060388:Support Vector Machine; D013647:Task Performance and Analysis; D055815:Young Adult