EEG-based Feedback Control for Emotion Regulation Inspired by the Bhagavad Gita

This study introduces an Electroencephalography
(EEG)-based feedback control framework for emotion
regulation, drawing inspiration from the Bhagavad Gita’s
teachings on self-mastery and balanced emotional response. By
integrating philosophical insights into modern neurotechnology,
the research aims to enhance self-regulation through real-time
detection and control of emotional states. The system analyzes
EEG data to extract meaningful statistical features that
represent emotional variations. Using the Kaggle EEG
Brainwave Dataset: Feeling Emotions, which includes signals
recorded via Muse headbands from TP9, AF7, AF8, and TP10
electrode sites during positive, neutral, and negative stimuli, the
proposed logistic regression classifier effectively categorizes
emotional states. The model achieved 95.58% accuracy, 89.27%
precision, 88.94% recall, and an F1-score of 89.02%,
demonstrating strong reliability. The fusion of Bhagavad Gitainspired
emotional control with computational EEG analysis
highlights the potential for developing intelligent, spiritually
aligned neurofeedback systems to promote mental stability and
emotional well-being.