Key result
The use of Discrete Cosine Transform along with a k-Nearest Neighbor classifier achieved a 72% accuracy in classifying EEG-based cognitive stress, outperforming Artificial Neural Network and Linear Discriminant Analysis.
Absolute Event Rate: 72% vs 44%
A cognitive stress recognition algorithm using single-electrode EEG data processed with DCT and KNN achieved a 72% classification rate.
Single-electrode EEG stress detection feasible in small cohorts; leaves open prospective validation before clinical adoption.
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.
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Wai Chong Chia (2014) studied Cognitive stress (n=25). Discrete Cosine Transform (DCT) with k-Nearest Neighbor (KNN) classifier vs. Artificial Neural Network (ANN) and Linear Discriminant Analysis (LDA) was evaluated on Average classification accuracy of cognitive stress. The use of Discrete Cosine Transform along with a k-Nearest Neighbor classifier achieved a 72% accuracy in classifying EEG-based cognitive stress, outperforming Artificial Neural Network and Linear Discriminant Analysis.
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