Emotion Recognition From EEG Using Higher Order Crossings

Panagiotis C. Petrantonakis · Leontios J. Hadjileontiadis

687 citations0/0 files storedIEEE Transactions on Information Technology in Biomedicine

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Face and Expression Recognition

Abstract

Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness, surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion experiences, recurring affective states, time-dependent emotional trends).

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References

1990Emotional Intelligence 2004THE MIRROR-NEURON SYSTEM 1997Affective Computing 2005International affective picture system (IAPS) : affective ratings of pictures and instruction manual 1997The media equation: how people treat computers, television, and new media like real people and places 2009The Mirror Neuron System 2001Toward machine emotional intelligence: analysis of affective physiological state 2004Analysis of emotion recognition using facial expressions, speech and multimodal information

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2018Spatial–Temporal Recurrent Neural Network for Emotion Recognition 2020EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM 2022Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review 2019GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition 2020An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals

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