A Hybrid Machine Learning Approach Utilizing PCA and ICA Features for Stress Classification

Electroencephalographic (EEG) signal-based personal identification systems have notable advantages and disadvantages. These systems heavily rely on the stability of EEG signals, which several factors. One of the primary factors affecting the stability of EEG signals is an individual's emotional state. Among emotional states, stress significantly impairs people's ability to perform daily tasks. This research aims to identify stress levels, classified as low (2) and high (1), using features from Independent Component Analysis (ICA) and Principal Component Analysis (PCA). Machine learning methods, including Decision Tree, k-Nearest Neighbors (k-NN), Naive Bayes, Support Vector Machine (SVM), and Ensemble techniques, are employed to classify the stress levels. The dataset comprises 40 EEG recordings from the Stroop color-word test, and the data is split using a random holdout function with a ratio of 80% for training and 20% for testing. This study examines the most effective features for identifying stress levels and compares the performance of various machine learning models. The experimental results demonstrate that PCA is the most effective feature extraction method, achieving an average accuracy of 0.718 in stress level classification. Among the machine learning models tested, the Ensemble method performs the best, achieving an accuracy of 0.770 when using PCA features and 0.745 with ICA features. This study highlights the importance of selecting optimal features and machine learning techniques for improving stress detection in EEG-based systems. Further improvements in classification accuracy may be achieved by incorporating additional physiological signals or refining feature extraction techniques.