Bispectral Analysis of EEG for Emotion Recognition | Zendy

Nitin Kumar | Zendy; Kaushikee Khaund | Zendy; Shyamanta M. Hazarika | Zendy

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Bispectral Analysis of EEG for Emotion Recognition

Author(s) -

Nitin Kumar,

Kaushikee Khaund,

Shyamanta M. Hazarika

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.04.062

Subject(s) - bispectrum , computer science , electroencephalography , arousal , valence (chemistry) , pattern recognition (psychology) , artificial intelligence , speech recognition , support vector machine , emotion recognition , feature (linguistics) , data set , gaussian , spectral density , psychology , neuroscience , telecommunications , linguistics , philosophy , physics , quantum mechanics

Emotion recognition from electroencephalogram (EEG) signals is one of the most challenging tasks. Bispectral analysis offers a way of gaining phase information by detecting phase relationships between frequency components and characterizing the non- Gaussian information contained in the EEG signals. In this paper, we explore derived features of bispectrum for quantification of emotions using a Valence-Arousal emotion model; and arrive at a feature vector through backward sequential search. Cross- validated accuracies of 64.84% for Low/High Arousal classification and 61.17% for Low/High Valence were obtained on the DEAP data set based on the proposed features; comparable to classification accuracies reported in the literature

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