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EEG emotion recognition based on an innovative information potential index

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Abstract

The recent exceptional demand for emotion recognition systems in clinical and non-medical applications has attracted the attention of many researchers. Since the brain is the primary object of understanding emotions and responding to them, electroencephalogram (EEG) signal analysis is one of the most popular approaches in affect classification. Previously, different approaches have been presented to benefit from brain connectivity information. We envisioned analyzing the interactions between brain electrodes with the information potential and providing a new index to quantify the connectivity matrix. The current study proposed a simple measure based on the cross-information potential between pairs of EEG electrodes to characterize emotions. This measure was tested for different EEG frequency bands to realize which EEG waves could be fruitful in recognizing emotions. Support vector machine and k-nearest neighbor (kNN) were implemented to classify four emotion categories based on two-dimensional valence and arousal space. Experimental results on the Database for Emotion Analysis using Physiological signals revealed a maximum accuracy of 90.14%, a sensitivity of 89.71%, and an F-score of 94.57% using kNN. The gamma frequency band obtained the highest recognition rates. Furthermore, low valence-low arousal was classified more effectively than other classes.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors”.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

    Atefeh Goshvarpour

  2. Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran

    Ateke Goshvarpour

  3. Health Technology Research Center, Imam Reza International University, Mashhad, Razavi Khorasan, Iran

    Ateke Goshvarpour

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  1. Atefeh Goshvarpour
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Correspondence to Ateke Goshvarpour.

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This article examined EEG signals of the DEAP (Koelstra et al. 2012) dataset, freely available in the public domain. This article does not contain any studies with human participants performed by any of the authors.”

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Goshvarpour, A., Goshvarpour, A. EEG emotion recognition based on an innovative information potential index. Cogn Neurodyn (2024). https://doi.org/10.1007/s11571-024-10077-1

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