发表机构
Faculty of Information Technology and Computing Sciences, Pori Campus, Tampere University（坦佩雷大学波里校区信息技术与计算科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用MUSE-S EEG头带采集5对受试者的EEG超扫描数据，通过提取频谱与跨相干特征训练CNN模型，实现休息与游戏状态的分类，仅用被试间跨相干特征也取得较高准确率。
AI 中文摘要
本研究展示了使用消费级EEG头带MUSE-S采集的EEG超扫描数据分类的初步结果。实验纳入5对受试者，记录方案包含3次双人俄罗斯方块游戏会话，其间穿插休息时段。对数据进行分段处理后，计算了10个频谱特征及跨相干特征，将这些特征整理为特征矩阵，并训练卷积神经网络（CNN）模型以区分休息与游戏状态。测试了两种特征集：完整特征集与仅包含被试间跨相干特征的子集。结果显示，使用完整特征集可完美区分休息与游戏时段；仅使用被试间跨相干特征时，训练数据分类准确率达94%，测试数据分类准确率为79%。
英文摘要
In this study preliminary results on the classification of EEG hyperscanning data acquired using MUSE-S consumer-level EEG headband are presented. Five pairs of subjects were involved and the recording protocol contained three two-person tetris game sessions alternating with relaxation periods. The data were segmented and ten spectral and cross-coherence features were calculated. The features were arranged into feature matrices and Convolutional Neural Network model was trained to discriminate between the relaxation and gaming. Two different feature sets - the full set and a set containing only inter subject cross-coherence features were tested. The results indicate that using the full feature set, relaxation and gaming periods were perfectly discriminated. Using only inter-subject cross-coherence features 94 % and 79 % classification accuracy for training and testing data was obtained, respectively.
Journal refT. Lipping, A. Sharif, M. Beiramvand and J. Turunen, "Detecting Interbrain Synchronization in EEG Hyperscanning with MUSE-S EEG Headband," 2025 IEEE 8th Portuguese Meeting on Bioengineering (ENBENG), Aveiro, Portugal, 2025, pp. 61-64
DOI：10.1109/ENBENG67130.2025.11199533
