Daşdemir Yaşar
Department of Computer Engineering, Erzurum Technical University, Erzurum, 25050, Turkey.
Heliyon. 2024 Oct 9;10(20):e38681. doi: 10.1016/j.heliyon.2024.e38681. eCollection 2024 Oct 30.
Human-computer interface (HCI) and electroencephalogram (EEG) signals are widely used in user experience (UX) interface designs to provide immersive interactions with the user. In the context of UX, EEG signals can be used within a metaverse system to assess user engagement, attention, emotional responses, or mental workload. By analyzing EEG signals, system designers can tailor the virtual environment, content, or interactions in real time to optimize UX, improve immersion, and personalize interactions. However, in this case, in addition to the signals' processing cost and classification accuracy, cybersickness in Virtual Reality (VR) systems needs to be resolved. At this point, channel selection methods can perform better for HCI and UX applications by reducing noisy and redundant information in generally unrelated EEG channels. For this purpose, a new method for EEG channel selection based on phase-locking value (PLV) analysis is proposed. We hypothesized that there are interactions between EEG channels in terms of PLV in repeated tasks in different trials of the emotion estimation experiment. Subsequently, frequency-based features were extracted. The features were classified by dividing them into bags using the Multiple-Instance Learning (MIL) variant. This study provides higher classification performance using fewer EEG channels for emotion prediction. The performance rate obtained in binary classification with the Random Forests (RF) algorithm is at a promising level of 99%. The proposed method achieved an accuracy of 99.38% for valence using all channels on the new dataset (VREMO) and 98.13% with channel selection. The benchmark dataset (DEAP) achieved accuracies of 98.16% using all channels and 98.13% with selected channels.
人机界面（HCI）和脑电图（EEG）信号在用户体验（UX）界面设计中被广泛应用，以提供与用户的沉浸式交互。在用户体验的背景下，EEG信号可在元宇宙系统中用于评估用户参与度、注意力、情绪反应或心理负荷。通过分析EEG信号，系统设计师可以实时调整虚拟环境、内容或交互，以优化用户体验、提高沉浸感并实现交互个性化。然而，在这种情况下，除了信号的处理成本和分类准确性外，虚拟现实（VR）系统中的网络晕动症问题也需要解决。此时，通道选择方法可以通过减少一般不相关EEG通道中的噪声和冗余信息，在HCI和UX应用中表现得更好。为此，提出了一种基于锁相值（PLV）分析的EEG通道选择新方法。我们假设在情绪估计实验的不同试验中的重复任务中，EEG通道之间在PLV方面存在相互作用。随后，提取了基于频率的特征。使用多实例学习（MIL）变体将这些特征划分为多个包进行分类。本研究使用较少的EEG通道进行情绪预测，提供了更高的分类性能。使用随机森林（RF）算法进行二分类时获得的准确率达到了令人满意的99%。所提出的方法在新数据集（VREMO）上使用所有通道进行效价分类时准确率为99.38%，使用通道选择时为98.13%。基准数据集（DEAP）使用所有通道时准确率为98.16%，使用选定通道时为98.13%。
