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

@article{citeulike:3225324, 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.}, author = {Logar, Vito and Belic, Ales and Koritnik, Blaz and Brezan, Simon and Zidar, Janez and Karba, Rihard and Matko, Drago}, citeulike-article-id = {3225324}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neunet.2008.03.012}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18562165}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18562165}, citeulike-linkout-3 = {http://www.sciencedirect.com/science/article/B6T08-4SH0XWK-1/2/f9b129a0fa69d275a315b4d8b09c9368}, doi = {10.1016/j.neunet.2008.03.012}, issn = {0893-6080}, journal = {Neural networks : the official journal of the International Neural Network Society}, keywords = {analysis, classification, eeg, erp, neuralnetwork}, month = {September}, number = {7}, pages = {881--887}, posted-at = {2008-09-11 13:37:55}, priority = {2}, title = {Using ANNs to predict a subject's response based on EEG traces.}, url = {http://dx.doi.org/10.1016/j.neunet.2008.03.012}, volume = {21}, year = {2008} }

TY - JOUR ID - citeulike:3225324 L3 - citeulike-article-id:3225324 N2 - 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. IS - 7 JF - Neural networks : the official journal of the International Neural Network Society SN - 0893-6080 EP - 887 TI - Using ANNs to predict a subject's response based on EEG traces. VL - 21 SP - 881 KW - analysis KW - classification KW - eeg KW - erp KW - neuralnetwork AU - Logar, Vito AU - Belic, Ales AU - Koritnik, Blaz AU - Brezan, Simon AU - Zidar, Janez AU - Karba, Rihard AU - Matko, Drago PY - 2008/09// UR - http://dx.doi.org/10.1016/j.neunet.2008.03.012 ER -