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Cognitive-Inspired Spectral Spatiotemporal Analysis for Emotion Recognition Utilizing Electroencephalography Signals

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Abstract

Background/Introduction

The rapid advancement of computer technologies, along with the significant role of emotions in daily life, has driven interest in intelligent emotion recognition systems. Electroencephalography (EEG) serves as a prominent objective tool in affective computing. However, effectively integrating multichannel EEG spatial and temporal information remains a critical challenge. This study introduces a novel emotion recognition model grounded in cognitive and biological principles, emphasizing the importance of spatiotemporal dynamics in emotional processing.

Methods

In this research, brain frequency bands were extracted through wavelet analysis, and the signals within predefined time windows were quantified. These features were then concatenated across distinct brain channels to create a comprehensive matrix representing spatiotemporal brain information. The matrix was characterized using both the summation of matrix cells and the highest singular value to optimize computational costs during classification. The resulting attributes were input into a classification module for emotion detection.

Results

Experimental results on the Database for Emotion Analysis using Physiological Signals (DEAP) achieved a maximum accuracy of 89.55%.

Conclusions

This work introduces a novel approach to analyzing and classifying EEG signals elicited by various emotional stimuli, demonstrating that the proposed model is competitive with the state-of-the-art classification schemes, thereby paving the way for future development of a robust spatiotemporal-based EEG emotion recognition system.

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Data Availability

“The DEAP dataset analyzed in this experiment is available at http://www.eecs.qmul.ac.uk/mmv/datasets/deap/index.html and can be assessed upon approval.”

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Funding

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

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Correspondence to Ateke Goshvarpour.

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Conflict of interest

“The author declares no conflict of interest.”

Ethical Approval

“This article examined EEG signals of the DEAP (Koelstra et al. 2012), freely available in the public domain. This article contains no studies with human participants performed by any authors.”

Informed Consent

“Not applicable.”

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Goshvarpour, A., Goshvarpour, A. Cognitive-Inspired Spectral Spatiotemporal Analysis for Emotion Recognition Utilizing Electroencephalography Signals. Cogn Comput 17, 2 (2025). https://doi.org/10.1007/s12559-024-10361-6

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  • DOI: https://doi.org/10.1007/s12559-024-10361-6

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