TY  - CONF
Y1  - 2017///
N1  - cited By 10; Conference of 3rd International Conference on Control, Electronics, Renewable Energy, and Communications, ICCREC 2017 ; Conference Date: 26 September 2017 Through 28 September 2017; Conference Code:134007
N2  - One of the popular features extraction methods for recognizing motor imagery EEG signal is Common Spatial Pattern (CSP). CSP is an algorithm that maximize the variance of one class and minimize the variance of other class simultaneously to discriminate two classes of multichannel EEG signals for classification purpose. However, CSP assumes that the signals on all EEG channels are functionally interconnected even though only spurious relationship due to artefact or noise. This study will conduct several investigations on the classification performance by imposing channels selection based on calculated energy on brain excitation calculation. The improvement strategy calculates the energy in each channel and the selection will be based on the energy level. In order to validate the performance of the proposed technique, three motor imagery data sets are employed including RIKEN, BCI Competition III Data set IVa, and BCI Competition IVData set I. In general, all these datasets are tested on the existing CSP and its variants with and without the proposed channel selection strategy. The existing techniques such as CSP, R-CSP (regularized CSP), and A-CSP (analytic CSP) are included in this study. The results show that the selected channels with higher energy can improve the CSP, R-CSP and A-CSP classification performance. Also, smaller size of selected channels in the area of motor cortex offers better performance with almost 75 channel reduction and 8 increase in accuracy. © 2017 IEEE.
A1  - Fauzi, H.
A1  - Shapiai, M.I.
A1  - Setiawan, N.A.
A1  - Jaafar, J.
A1  - Mustafa, M.
SP  - 33
EP  - 39
SN  - 9781538616673
TI  - Channel selection for common spatial pattern Based on energy calculation of motor imagery EEG signal
VL  - 2017-J
PB  - Institute of Electrical and Electronics Engineers Inc.
KW  - Brain computer interface
KW  -  Channel selection; Classification performance; Common spatial patterns; energy; Energy calculation; Features extraction; Improvement strategies; Motor imagery eeg signals
KW  -  Biomedical signal processing
ID  - scholars8059
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045974421&doi=10.1109%2fICCEREC.2017.8226692&partnerID=40&md5=de42b29818c43eb7e4bacfd38d1adec2
AV  - none
ER  -