Work

EEG headbands vs caps: How many electrodes do I need to detect emotions? The case of the MUSE headband

Year: 2024

Type: article

Abstract: BACKGROUND: In the realm of emotion detection, comfort and portability play crucial roles in enhancing user experiences. However, few works study the reduction in the number of electrodes used to detect emotions, and none of them compare the location of these electrodes with a commercial low-cost headband. METHODS: This work explores the potential of wearable EEG devices, specifically the Muse S headband, for emotion classification in terms of valence and arousal. We conducted a direct comparison between the Muse S, with its only four electrodes, and the DEAP dataset, which employs 32-electrode in a more intrusive headset. DEAP is a benchmark dataset constructed by emotions elicited by music. Our methodology focused on utilizing raw data and extracting four common frequency ranges. In particular, we select from DEAP the 4 electrodes that are similar to those in the Muse S. Additionally, we created a dataset using the Muse S, where we segmented the complete video into fixed-size temporal windows. Our 4-electrodes dataset uses film clips to elicit emotions, classified according to the Self-Assessment Manikin. RESULTS: Our findings indicate that the Muse S, despite its limited electrode count, can effectively discriminate between high and low valence/arousal emotions with accuracy comparable to the accuracy obtained with all the DEAP electrodes. The Gamma band emerged as particularly effective for valence detection. Using a Muse device and raw data, the best performance achieved a G-Mean only 1-2% lower than that of the DEAP dataset, demonstrating that comparable results can be obtained with a simplified setup. CONCLUSIONS: While the Muse-S did not reach DEAP in terms of outcomes, it proved to be a viable, lower-cost, less intrusive alternative, and adaptable for everyday use. The dataset created for this study is publicly available at https://doi.org/10.5281/zenodo.8431451.

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2024.109463

Authors: Francisco M. Garcia-Moreno, Marta Badenes-Sastre, Francisca Expósito, Maria Jose Rodriguez-Fortiz, Maria Bermudez-Edo

Institution: Universidad de Granada

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,640