EEG based emotion recognition system using MFDFA as feature extractor

Sananda Paul · Ankita Mazumder · Poulami Ghosh · Dewaki Nandan Tibarewala · G. Vimalarani

40 citations0/0 files storedInternational Conference on Robotics, Automation, Control and Embedded Systems (RACE)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Emotion and Mood Recognition EEG and Brain-Computer Interfaces Muscle activation and electromyography studies

Abstract

Emotion is a complex set of interactions among subjective and objective factors governed by neural/hormonal systems resulting in the arousal of feelings and generate cognitive processes, activate physiological changes such as behavior. Emotion recognition can be correctly done by EEG signals. Electroencephalogram (EEG) is the direct reflection of the activities of hundreds and millions of neurons residing within the brain. Different emotion states create distinct EEG signals in different brain regions. Therefore EEG provides reliable technique to identify the underlying emotion information. This paper proposes a novel approach to recognize users emotions from electroencephalogram (EEG) signals. Audio signals are used as stimuli to elicit positive and negative emotions of subjects. For eight healthy subjects, EEG signals are acquired using seven channels of an EEG amplifier. The result reveal that frontal, temporal and parietal regions of the brain are relevant to positive emotion recognition and frontal and parietal regions are activated in case of negative emotion identification. After proper signal processing of the raw EEG, for the whole frequency bands the features are extracted from each channel of the EEG signals by Multifractral Detrended Fluctuation Analysis (MFDFA) method. We introduce an effective classifier named Support Vector Machine (SVM) to categorize the EEG feature space related to various emotional states into their respective classes. Next, we compare Support Vector Machine (SVM) with various other methods like Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and K Nearest Neighbor (KNN). The average classification accuracy of SVM for positive emotions on the whole frequency bands is 84.50%, while the accuracy of QDA is 76.50% and with LDA 75.25% and KNN is only 69.625% whereas, for negative emotions it is 82.50%, while for QDA is 72.375% and with LDA 65.125% and KNN is only 70.50%.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

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

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 40 citations exist upstream.

References

1997Affective Computing 2002Multifractal detrended fluctuation analysis of nonstationary time series 1927The James-Lange Theory of Emotions: A Critical Examination and an Alternative Theory 1963Emotion and Personality 2012Introduction to Multifractal Detrended Fluctuation Analysis in Matlab 1961Emotion and Personality 2011A Novel Emotion Elicitation Index Using Frontal Brain Asymmetry for Enhanced EEG-Based Emotion Recognition 2009Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of EEG

Cited by

2018Emotional State Recognition with EEG Signals Using Subject Independent Approach 2020Review on Stimuli Presentation for Affect Analysis Based on EEG

Explore the map →

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

Imported from OpenAlex (CC0) — nothing here is generated.