Brain Activity Monitoring for Stress Analysis through EEG Dataset using Machine Learning
Keywords:
KNN, DT, ANN, Stress Analysis, EEG Signal AnalysisAbstract
To determine the possible conditions of users during task execution, researchers employ psychological feedback tools such as skin conduction (S), electroencephalography (EEG), and electrocardiography (ECG). A set of protocols is developed via a series of cognitive studies in which participants complete a series of intellectually challenging activities. The high time resolution of electroencephalography (EEG) allows for continuous monitoring of brain conditions such as human mental effort, emotions, and stress levels. The main goal is to evaluate the efficiency of cognitive stress recognition systems. Lack of suitable EEG channels and bands selection for stress recognition system. Using brain interface for EEG with as few channels as possible. Quick Fourier Transform is a dimension reduction technique used to reduce the amount of data from the root. The acquired FFT and correlation-based feature subset selection methods were used to train three model taxonomic algorithms: SVM, K-Nearest Neighbor (KNN), Decision Tree (DT), and artificial Neural Networks (NN). We can expect brain monitoring such as stress to be cost effective and capable of reliable patient monitoring.
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