TY - GEN
T1 - Fuzzy logic-based automatic alertness state classification using multi-channel EEG data
AU - Al-Ani, Ahmed
AU - Mesbah, Mostefa
AU - Van Dun, Bram
AU - Dillon, Harvey
PY - 2013
Y1 - 2013
N2 - This paper represents an attempt to automatically classify alertness state using information extracted from multi-channel EEG. To reduce the amount of data and improve the performance, a channel selection method based on support vector machine (SVM) classifier has been performed. The features used for the EEG channel selection process and subsequently for alertness classification represent the energy values of the five EEG rhythms; namely δ, θ, α, β and γ. In order to identify the feature/channel combination that leads to the best alertness state classification performance, we used a fuzzy rule-based classification system (FRBCS) that utilizes differential evolution in constructing the rules. The results obtained using the FRBCS were found to be comparable to those of SVM but with the added advantage of revealing the rhythm/channel combination associated with each alertness state.
AB - This paper represents an attempt to automatically classify alertness state using information extracted from multi-channel EEG. To reduce the amount of data and improve the performance, a channel selection method based on support vector machine (SVM) classifier has been performed. The features used for the EEG channel selection process and subsequently for alertness classification represent the energy values of the five EEG rhythms; namely δ, θ, α, β and γ. In order to identify the feature/channel combination that leads to the best alertness state classification performance, we used a fuzzy rule-based classification system (FRBCS) that utilizes differential evolution in constructing the rules. The results obtained using the FRBCS were found to be comparable to those of SVM but with the added advantage of revealing the rhythm/channel combination associated with each alertness state.
KW - Alertness classification
KW - EEG
KW - Fuzzy rule-based system
UR - http://www.scopus.com/inward/record.url?scp=84893411139&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893411139&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-42054-2_23
DO - 10.1007/978-3-642-42054-2_23
M3 - Conference contribution
AN - SCOPUS:84893411139
SN - 9783642420535
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 176
EP - 183
BT - Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
T2 - 20th International Conference on Neural Information Processing, ICONIP 2013
Y2 - 3 November 2013 through 7 November 2013
ER -