Abstract—This paper demonstrates electroencephalogram
(EEG) analysis in MATLAB environment with the objective to
investigate effectiveness of cognitive stress recognition
algorithm using EEG from single-electrode BCI. 25 subjects’
EEG were recorded in MATLAB with the use of Stroop
color-word test as stress inducer. Questionnaire on subjects’
self-perceived stress scale during Stroop test were gathered as
classification’s target output. The main analysis tool used were
MATLAB, coupled with the use of Discrete Cosine Transform
(DCT) as dimension reduction technique to reduce data size
down to 2% of the origin. Three pattern classification
algorithms’ – Artificial Neural Network (ANN), k-Nearest
Neighbor (KNN) and Linear Discriminant Analysis (LDA) were
trained using the resulted 2% DCT coefficients. Our study
discovered the use of DCT along with KNN offers highest
average classification rate of 72% compared to ANN and LDA.
Index Terms—BCI, EEG, MATLAB, stress recognition.
Chee-Keong Alfred Lima and Wai Chong Chia are with the Faculty of
Science and Technology, Sunway University, Selangor, Malaysia (e-mail:
09065434@imail.sunway.edu.my; waichongc@sunway.edu.my).
[PDF]
Cite: Chee-Keong Alfred Lim and Wai Chong Chia, "Analysis of Single-Electrode EEG Rhythms Using MATLAB to Elicit Correlation with Cognitive Stress," International Journal of Computer Theory and Engineering vol. 7, no. 2, pp. 149-155, 2015.
