Using ANNs to predict a subject's response based on EEG traces

Authors:
Vito Logar;Aleš Belič;Bla Koritnik;Simon Brean;Janez Zidar;Rihard Karba;Drago Matko
Affiliations:
University of Ljubljana, Faculty of Electrical Engineering, Traška 25, SI-1000, Ljubljana, Slovenia;University of Ljubljana, Faculty of Electrical Engineering, Traška 25, SI-1000, Ljubljana, Slovenia;University Medical Centre Ljubljana, Division of Neurology, Institute of Clinical Neurophysiology, Zaloška 7, SI-1525, Ljubljana, Slovenia;University Medical Centre Ljubljana, Division of Neurology, Institute of Clinical Neurophysiology, Zaloška 7, SI-1525, Ljubljana, Slovenia;University Medical Centre Ljubljana, Division of Neurology, Institute of Clinical Neurophysiology, Zaloška 7, SI-1525, Ljubljana, Slovenia;University of Ljubljana, Faculty of Electrical Engineering, Traška 25, SI-1000, Ljubljana, Slovenia;University of Ljubljana, Faculty of Electrical Engineering, Traška 25, SI-1000, Ljubljana, Slovenia
Venue:
Neural Networks
Year:
2008

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Abstract

Numerous reports have shown that performing working-memory tasks causes an elevated rhythmic coupling in different areas of the brain; it has been suggested that this indicates information exchange. Since the information exchanged is encoded in brain waves and measurable by electroencephalography (EEG) it is reasonable to assume that it can be extracted with an appropriate method. In our study we made an attempt to extract the information using an artificial neural network (ANN), which can be considered as a stimulus-response model with a state observer. The EEG was recorded from three subjects while they performed a modified Sternberg task that required them to respond to each task with the answer ''true'' or ''false''. The study revealed that a stimulus-response model can successfully be identified by observing phase-demodulated theta-band EEG signals 1 s prior to a subject's answer. The results also showed that it was possible to predict the answers from the EEG signals with an average reliability of 75% for all the subjects. From this we concluded that it is possible to observe the system states and thus predict the correct answer using the EEG signals as inputs.