TY - GEN
T1 - ERPs-based attention analysis using continuous wavelet transform
T2 - 14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016
AU - Karatzia, Anastasia
AU - Petsani, Despoina
AU - Kaza, Chrysoula
AU - Argyriou, Christos Rafail
AU - Galanopoulos, Anastasios
AU - Karaiskou, Angeliki Ιlektra
AU - Triantaris, Pavlos
AU - Xygonakis, Ioannis
AU - Papadaniil, Chrysa
AU - Hadjileontiadis, Leontios J.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Evoked Related Potentials (ERPs) analysis for distinguishing between bottom-up and top-down attention, using 256-channel EEG signals obtained by measurements on humans, is investigated here. The three main ERPs, i.e., N170, P300, N400 were obtained after appropriate time windows selection for different response peaks and averaging EEG waveforms from all trials for each channel. Following that, Continuous Wavelet Transform (CWT) was applied on each ERP waveform and a vector of six morphological CWT-based features was constructed and fed to two well-known classifiers, i.e., SVM and k-NN. The selected ERPs were drawn from those channels where they are known to be observed more frequently. The experimental results have shown that P300 provides higher classification rates than N170 and N400, reaching a classification accuracy of 76%. Moreover, SVM and k-NN showed similar performance, with the latter being slightly more efficient. Finally, gender factorization of data contributed to a maximum classification accuracy of 80%. The proposed analysis paves the way for better understanding of the activity of the brain in different attention scenarios as reflected in the CWT domain, exploring the time-frequency characteristics of the related ERPs, contributing to the detection of potential attention disorders.
AB - Evoked Related Potentials (ERPs) analysis for distinguishing between bottom-up and top-down attention, using 256-channel EEG signals obtained by measurements on humans, is investigated here. The three main ERPs, i.e., N170, P300, N400 were obtained after appropriate time windows selection for different response peaks and averaging EEG waveforms from all trials for each channel. Following that, Continuous Wavelet Transform (CWT) was applied on each ERP waveform and a vector of six morphological CWT-based features was constructed and fed to two well-known classifiers, i.e., SVM and k-NN. The selected ERPs were drawn from those channels where they are known to be observed more frequently. The experimental results have shown that P300 provides higher classification rates than N170 and N400, reaching a classification accuracy of 76%. Moreover, SVM and k-NN showed similar performance, with the latter being slightly more efficient. Finally, gender factorization of data contributed to a maximum classification accuracy of 80%. The proposed analysis paves the way for better understanding of the activity of the brain in different attention scenarios as reflected in the CWT domain, exploring the time-frequency characteristics of the related ERPs, contributing to the detection of potential attention disorders.
KW - Bottom-up
KW - Classification
KW - CWT
KW - ERPs
KW - Top-down
UR - http://www.scopus.com/inward/record.url?scp=84968531488&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-32703-7_2
DO - 10.1007/978-3-319-32703-7_2
M3 - Conference contribution
AN - SCOPUS:84968531488
SN - 9783319327013
T3 - IFMBE Proceedings
SP - 9
EP - 14
BT - XIV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016
A2 - Kyriacou, Efthyvoulos
A2 - Christofides, Stelios
A2 - Pattichis, Constantinos S.
PB - Springer Verlag
Y2 - 31 March 2016 through 2 April 2016
ER -