By Topic

Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Petrantonakis, P.C. ; Dept. of Electr. & Comput. Eng., Aristotle Univ. of Thessaloniki, Thessaloniki, Greece ; Hadjileontiadis, L.J.

This paper aims at providing a new feature extraction method for a user-independent emotion recognition system, namely, HAF-HOC, from electroencephalograms (EEGs). A novel filtering procedure, namely, Hybrid Adaptive Filtering (HAF), for an efficient extraction of the emotion-related EEG-characteristics was developed by applying Genetic Algorithms to the Empirical Mode Decomposition-based representation of EEG signals. In addition, Higher Order Crossings (HOCs) analysis was employed for feature extraction realization from the HAF-filtered signals. The introduced HAF-HOC scheme incorporated four different classification methods to accomplish a robust emotion recognition performance. Through a series of facial-expression image projection, as a Mirror Neuron System-based emotion elicitation process, EEG data related to six basic emotions (happiness, surprise, anger, fear, disgust, and sadness) have been acquired from 16 healthy subjects using three EEG channels. Experimental results from the application of the HAF-HOC to the collected EEG data and comparison with previous approaches have shown that the HAF-HOC scheme clearly surpasses the latter in the field of emotion recognition from brain signals for the discrimination of up to six distinct emotions, providing higher classification rates up to 85.17 percent. The promising performance of the HAF-HOC surfaces the value of EEG signals within the endeavor of realizing more pragmatic, affective human-machine interfaces.

Published in:

Affective Computing, IEEE Transactions on  (Volume:1 ,  Issue: 2 )