Using ANNs to predict a subject's response based on EEG traces.
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.
Logar
Vito
V
University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, SI-1000, Ljubljana, Slovenia. Vito.Logar@fe.uni-lj.si
Belic
Ales
A
Koritnik
Blaz
B
Brezan
Simon
S
Zidar
Janez
J
Karba
Rihard
R
Matko
Drago
D
D000328:Adult; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D008297:Male; D008570:Memory, Short-Term / Q000502:physiology; D008954:Models, Biological; D016571:Neural Networks, Computer; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult