Evaluating an Elevated Signal-to-Noise Ratio in EEG Emotion Recognition

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Author(s): Zachary Estreito (University of Nevada, Reno, USA), Vinh Le (University of Nevada, Reno, USA), Frederick C. Harris Jr. (University of Nevada, Reno, USA)and Sergiu M. Dascalu (University of Nevada, Reno, USA)
Copyright: 2024
Volume: 12
Issue: 1
Pages: 15
Source title: International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.333161

Keywords: Computer Science & IT / Information Science Reference / Systems & Software Design / Systems and Software Engineering

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Abstract

Predicting valence and arousal values from EEG signals has been a steadfast research topic within the field of affective computing or emotional AI. Although numerous valid techniques to predict valence and arousal values from EEG signals have been established and verified, the EEG data collection process itself is relatively undocumented. This creates an artificial learning curve for new researchers seeking to incorporate EEGs within their research workflow. In this article, a study is presented that illustrates the importance of a strict EEG data collection process for EEG affective computing studies. The work was evaluated by first validating the effectiveness of a machine learning prediction model on the DREAMER dataset, then showcasing the lack of effectiveness of the same machine learning prediction model on cursorily obtained EEG data.

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Evaluating an Elevated Signal-to-Noise Ratio in EEG Emotion Recognition

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