Enhancing Meditation Techniques and Insights Using Feature Analysis of Electroencephalography (EEG)
Al-Mustansiriyah Journal of Science March 30, 2024 Zahraa Maki Khadam, Abbas Abdulazeez Abdulhameed, Ahmed Hammad
Using a Muse 2 EEG headset connected via Bluetooth to a meditation app, this work describes a method for collecting, preprocessing, and classifying EEG data with IoT-based techniques. After preprocessing (removing redundant columns, handling missing data, normalizing, filtering), statistical features (mean, standard deviation, entropy) were extracted from the EEG signals. Three machine-learning models were trained: Support Vector Machine (SVM), Random Forest, and Multi-Layer Perceptron (MLP). The Random Forest model achieved the highest accuracy at 0.999, followed by MLP at 0.99 and SVM at 0.959. The authors propose this IoT-integrated approach as a viable method for brain-computer interfaces and meditation practice.