TY - GEN
T1 - Human movement intentions based on EEG using brain computer interfaces
AU - Ishak, Mohamad Khairi
AU - Dyson, Matthew
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/24
Y1 - 2015/11/24
N2 - This paper proposes classifying the signal of movement intention and identifying feature selection and translation algorithms. Furthermore, this paper will select the most appropriate algorithms for the feature classification of the signal of movement intentions. The study uses signals previously recorded in the BCI lab. Feature selection and classification were based on the Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). The results of classification show that LDA classifier recorded the highest accuracy in 3 and 4-class of movement in comparison to the SVM. LDA classified the 4-class of movements at central channel and single channel with the average accuracy of 43.75% and 42%. Overall, LDA performed better result in 3-class of movement, with an average accuracy 62%.
AB - This paper proposes classifying the signal of movement intention and identifying feature selection and translation algorithms. Furthermore, this paper will select the most appropriate algorithms for the feature classification of the signal of movement intentions. The study uses signals previously recorded in the BCI lab. Feature selection and classification were based on the Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). The results of classification show that LDA classifier recorded the highest accuracy in 3 and 4-class of movement in comparison to the SVM. LDA classified the 4-class of movements at central channel and single channel with the average accuracy of 43.75% and 42%. Overall, LDA performed better result in 3-class of movement, with an average accuracy 62%.
KW - EEG
KW - Linear Discriminant Analysis (LDA)
KW - Support Vector Machine (SVM) and movement intention
UR - https://www.scopus.com/pages/publications/84960890071
U2 - 10.1109/ICCEREC.2015.7337054
DO - 10.1109/ICCEREC.2015.7337054
M3 - Conference contribution
AN - SCOPUS:84960890071
T3 - ICCEREC 2015 - International Conference on Control, Electronics, Renewable Energy and Communications
SP - 58
EP - 62
BT - ICCEREC 2015 - International Conference on Control, Electronics, Renewable Energy and Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Control, Electronics, Renewable Energy and Communications, ICCEREC 2015
Y2 - 27 August 2015 through 28 August 2015
ER -