School of Engineering, Tokyo Institute of Technology, Yokohama 226-8503, Japan.
School of Computing, Tokyo Institute of Technology, Yokohama 226-8503, Japan.
Sensors (Basel). 2023 Dec 21;24(1):45. doi: 10.3390/s24010045.
To explore whether temporal electroencephalography (EEG) traits can dissociate the physical properties of touching objects and the congruence effects of cross-modal stimuli, we applied a machine learning approach to two major temporal domain EEG traits, event-related potential (ERP) and somatosensory evoked potential (SEP), for each anatomical brain region. During a task in which participants had to identify one of two material surfaces as a tactile stimulus, a photo image that matched ('congruent') or mismatched ('incongruent') the material they were touching was given as a visual stimulus. Electrical stimulation was applied to the median nerve of the right wrist to evoke SEP while the participants touched the material. The classification accuracies using ERP extracted in reference to the tactile/visual stimulus onsets were significantly higher than chance levels in several regions in both congruent and incongruent conditions, whereas SEP extracted in reference to the electrical stimulus onsets resulted in no significant classification accuracies. Further analysis based on current source signals estimated using EEG revealed brain regions showing significant accuracy across conditions, suggesting that tactile-based object recognition information is encoded in the temporal domain EEG trait and broader brain regions, including the premotor, parietal, and somatosensory areas.
为了探索时间域脑电图（EEG）特征是否可以区分触摸物体的物理属性和跨模态刺激的一致性效应，我们应用机器学习方法对每个解剖脑区的两个主要时间域 EEG 特征，事件相关电位（ERP）和体感诱发电位（SEP）进行了研究。在一项任务中，参与者必须识别两个材料表面中的一个作为触觉刺激，同时给予与他们正在触摸的材料匹配（“一致”）或不匹配（“不一致”）的照片图像作为视觉刺激。在参与者触摸材料时，通过对右腕正中神经施加电刺激来诱发 SEP。使用 ERP 提取与触觉/视觉刺激开始时的分类准确率在几个区域都显著高于一致和不一致条件下的随机水平，而使用 SEP 提取与电刺激开始时的分类准确率则没有显著提高。基于使用 EEG 估计的电流源信号的进一步分析表明，在不同条件下存在显著准确率的脑区，这表明基于触觉的物体识别信息是在时间域 EEG 特征和更广泛的脑区（包括运动前区、顶叶和体感区）中进行编码的。
