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

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

Zachary Estreito, Vinh Le, Frederick C. Harris Jr., Sergiu M. Dascalu
Copyright: © 2024 |Pages: 15
DOI: 10.4018/IJSI.333161
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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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Background

Valence–Arousal Model

Russell’s valence–arousal model (Russell et al., 1979) is a human emotion classification model consisting of two dimensions: valence and arousal. Valence represents the positivity of the emotion being felt, with positive emotions existing on one side of the axis and negative emotions on the other side. Arousal represents the degree of stimulation of the emotion being felt, with high-stimulation emotions being placed on one side of the axis perpendicular to the valence axis and low-stimulation emotions on the other side. The combination of these two axes allows emotions to be categorized into four unique quadrants: high valence with high arousal, high valence with low arousal, low valence with high arousal, and low valence with low arousal. These quadrants provide a convenient way to group similar emotions. With the quadrants drawn out, discrete emotions can then be placed in their associated quadrants, as seen in Figure 1.

Figure 1.

The representation of emotions in the valence–arousal model

IJSI.333161.f01

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