TY - GEN
T1 - Classification of P300 event-related potentials using wavelet transform, MLP, and soft margin SVM
AU - Tjandrasa, Handayani
AU - Djanali, Supeno
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - Brain-computer interface is a communication mechanism between EEG signals and a computer, such that the system can capture the brain intention without involving motoric and muscular neurons. This study utilized the EEG recordings of four disabled subjects during repeated stimuli using a six-choice P300 paradigm. The EEG signals were processed with a Butterworth bandpass filter and Wavelet Transform, divided into two categories of the target and non-target trials. The EEG data were improved by removing the high amplitude fluctuation of the signals around the end of each file. The Wavelet Transform was implemented using Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT). The target and non-target trials were averaged for every five trials, and the averaged non-target trials were reduced further by selecting one of every five consecutive data. The reduced target and non-target trial data were classified using multilayer perceptron and support vector machine. Using SWT, multilayer perceptron gave the maximum accuracy, sensitivity, and specificity of 96.4%, 96.6%, 96.2% respectively, and support vector machine obtained the maximum accuracy of 98.2%, sensitivity of 100%, and specificity of 96.4%. While using DWT, the best performance of multilayer perceptron gave the accuracy, sensitivity, and specificity of 94.5%, 100%, 89.3% respectively, and support vector machine had the maximum accuracy of 98.2%, sensitivity of 96.4%, and specificity of 100%.
AB - Brain-computer interface is a communication mechanism between EEG signals and a computer, such that the system can capture the brain intention without involving motoric and muscular neurons. This study utilized the EEG recordings of four disabled subjects during repeated stimuli using a six-choice P300 paradigm. The EEG signals were processed with a Butterworth bandpass filter and Wavelet Transform, divided into two categories of the target and non-target trials. The EEG data were improved by removing the high amplitude fluctuation of the signals around the end of each file. The Wavelet Transform was implemented using Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT). The target and non-target trials were averaged for every five trials, and the averaged non-target trials were reduced further by selecting one of every five consecutive data. The reduced target and non-target trial data were classified using multilayer perceptron and support vector machine. Using SWT, multilayer perceptron gave the maximum accuracy, sensitivity, and specificity of 96.4%, 96.6%, 96.2% respectively, and support vector machine obtained the maximum accuracy of 98.2%, sensitivity of 100%, and specificity of 96.4%. While using DWT, the best performance of multilayer perceptron gave the accuracy, sensitivity, and specificity of 94.5%, 100%, 89.3% respectively, and support vector machine had the maximum accuracy of 98.2%, sensitivity of 96.4%, and specificity of 100%.
KW - Discrete wavelet transform
KW - EEG
KW - Multilayer perceptron
KW - P300 event-related potential
KW - Soft margin support vector machine
KW - Stationary wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=85049795318&partnerID=8YFLogxK
U2 - 10.1109/ICACI.2018.8377481
DO - 10.1109/ICACI.2018.8377481
M3 - Conference contribution
AN - SCOPUS:85049795318
T3 - Proceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018
SP - 343
EP - 347
BT - Proceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Advanced Computational Intelligence, ICACI 2018
Y2 - 29 March 2018 through 31 March 2018
ER -