EEG Based Emotion Classification Mechanism in BCI

Abstract

Psychological changes in humans are the result of emotions which occur due to activities in daily life. To understand these changes in behavioral pattern, research on a ective computing has emerged. Emotions are an integral part of our daily lives, based on which in this paper an investigation have been made to analyze the impact of positive and negative emotions using Electroencephalogram (EEG). Three classes of emotions namely calm, anger and happiness have been studied. The EEG signals are recorded in real time from 10 subjects while watching di erent emotions video clips of 2 minutes each. Next, the fractal dimension feature has been extracted from raw EEG. To further detect emotional states, the extracted features have been classified using Support Vector Machine (SVM) with radial basis function (RBF) kernel with an average accuracy of 60%. The proposed methodology shows that emotions recognition is possible from EEG signals.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 82 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.