Garcia-Moreno Francisco M, Badenes-Sastre Marta, Expósito Francisca, Rodriguez-Fortiz Maria Jose, Bermudez-Edo Maria
Department of Software Engineering, Computer Science School, University of Granada, Granada, Spain; Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain.
Department of Social Psychology, University of Granada, Granada, Spain.
Comput Biol Med. 2025 Jan;184:109463. doi: 10.1016/j.compbiomed.2024.109463. Epub 2024 Nov 27.
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.
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.
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.
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.
在情感检测领域，舒适性和便携性对于提升用户体验起着至关重要的作用。然而，很少有研究探讨如何减少用于检测情感的电极数量，并且没有一项研究将这些电极的位置与商用低成本头带进行比较。
本研究探索了可穿戴式脑电图（EEG）设备，特别是Muse S头带，在情感效价和唤醒度分类方面的潜力。我们将仅有四个电极的Muse S与采用更具侵入性的头戴式设备且有32个电极的DEAP数据集进行了直接比较。DEAP是一个由音乐引发的情感构建的基准数据集。我们的方法侧重于利用原始数据并提取四个常见频率范围。具体而言，我们从DEAP中选择了与Muse S中相似的4个电极。此外，我们使用Muse S创建了一个数据集，将完整视频分割为固定大小的时间窗口。我们的4电极数据集使用电影片段来引发情感，并根据自我评估人体模型进行分类。
我们的研究结果表明，尽管Muse S的电极数量有限，但它能够有效地区分高、低情感效价/唤醒度的情绪，其准确率与使用DEAP所有电极获得的准确率相当。伽马波段在效价检测方面表现得尤为有效。使用Muse设备和原始数据，所取得的最佳性能的G均值仅比DEAP数据集低1 - 2%，这表明通过简化设置可以获得可比的结果。
虽然Muse - S在结果方面未达到DEAP的水平，但它被证明是一种可行的、低成本、侵入性较小的替代方案，适用于日常使用。本研究创建的数据集可在https://doi.org/10.5281/zenodo.8431451上公开获取。
