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Innovative Poincare’s plot asymmetry descriptors for EEG emotion recognition

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Abstract

Given the importance of emotion recognition in both medical and non-medical applications, designing an automatic system has captured the attention of several scholars. Currently, EEG-based emotion recognition has a special position, which has not fulfilled the desired accuracy rates yet. This experiment intended to provide novel EEG asymmetry measures to improve emotion recognition rates. Four emotional states have been classified using the k-nearest neighbor (kNN), support vector machine, and Naïve Bayes. Feature selection has been performed, and the role of employing a different number of top-ranked features on emotion recognition rates has been assessed. To validate the efficiency of the proposed scheme, two public databases, including the SJTU Emotion EEG Dataset-IV (SEED-IV) and a Database for Emotion Analysis using Physiological signals (DEAP) were evaluated. The experimental results indicated that kNN outperformed the other classifiers with a maximum accuracy of 95.49 and 98.63% using SEED-IV and DEAP datasets, respectively. In conclusion, the results of the proposed novel EEG-asymmetry measures make the framework a superior one compared to the state-of-art EEG emotion recognition approaches.

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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, Rezvan Campus, Phalestine Sq., 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 SEED-IV dataset (Zheng et al. 2019) and the DEAP database (Koelstra et al. 2012), which are 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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Informed consent was obtained from all individual participants included in the study (Zheng et al. 2019; Koelstra et al. 2012).

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Goshvarpour, A., Goshvarpour, A. Innovative Poincare’s plot asymmetry descriptors for EEG emotion recognition. Cogn Neurodyn 16, 545–559 (2022). https://doi.org/10.1007/s11571-021-09735-5

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