A machine learning approach for detection of claustrophobic brain activity in electroencephalography.

Claustrophobia, a phobia with a specific unreasonable and excessive fear of enclosed spaces, can have a considerable impact on an individual's life. Electroencephalography (EEG) has been a tool with potential for studying neural processes in anxiety disorders including claustrophobia. In this work, a machine learning algorithm for differentiation between claustrophobic and healthy controls using EEG signals is presented. EEG data were collected from 22 participants under controlled conditions, and preprocessing included filtering, artifact removal, and feature extraction using relative Power Spectral Density (rPSD) across five frequency bands: delta, theta, alpha, beta, and gamma. Classical machine learning models such as Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Tree (DT), and Random Forest (RF) were applied to assess their suitability in this domain. In addition, deep learning models including Multi-Layer Perceptron (MLP) and a Convolutional Neural Network with Bidirectional Long Short-Term Memory (CNN-BiLSTM) integration were utilized for their capacity to capture complex temporal and spatial patterns in EEG data. Performance testing with five-fold cross-validation revealed that MLP and CNN-BiLSTM performed best in terms of accuracy in classification, with a 95.15% ± 0.77 when all bands of frequencies were combined together. An analysis of brain regions revealed frontal and temporal regions to differentiate between claustrophobic and non-claustrophobic subjects, and beta and theta bands played a significant role in distinguishing between them. These observations unveil high potential for EEG-based machine learning algorithms in objective evaluation of claustrophobia, and propose opportunities for future development in its therapy and diagnosis.