Electrical and Computer Engineering Department, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Bioengineering Department, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Sci Rep. 2020 Nov 27;10(1):20755. doi: 10.1038/s41598-020-77439-7.
Trial-by-trial texture classification analysis and identifying salient texture related EEG features during active touch that are minimally influenced by movement type and frequency conditions are the main contributions of this work. A total of twelve healthy subjects were recruited. Each subject was instructed to use the fingertip of their dominant hand's index finger to rub or tap three textured surfaces (smooth flat, medium rough, and rough) with three levels of movement frequency (approximately 2, 1 and 0.5 Hz). EEG and force data were collected synchronously during each touch condition. A systematic feature selection process was performed to select temporal and spectral EEG features that contribute to texture classification but have low contribution towards movement type and frequency classification. A tenfold cross validation was used to train two 3-class (each for texture and movement frequency classification) and a 2-class (movement type) Support Vector Machine classifiers. Our results showed that the total power in the mu (8-15 Hz) and beta (16-30 Hz) frequency bands showed high accuracy in discriminating among textures with different levels of roughness (average accuracy > 84%) but lower contribution towards movement type (average accuracy < 65%) and frequency (average accuracy < 58%) classification.
本研究的主要贡献在于对主动触觉过程中的纹理进行逐次分类分析，并识别出受运动类型和频率条件影响最小的显著纹理相关 EEG 特征。共招募了 12 名健康受试者。每位受试者均被指示使用其优势手食指的指尖摩擦或轻敲三种纹理表面（光滑平面、中等粗糙和粗糙），运动频率有三个级别（约 2Hz、1Hz 和 0.5Hz）。在每次触摸条件下，EEG 和力数据均被同步采集。我们采用系统的特征选择过程，选择对纹理分类有贡献但对运动类型和频率分类贡献较低的时频 EEG 特征。我们使用 10 倍交叉验证来训练两个 3 类（每个用于纹理和运动频率分类）和一个 2 类（运动类型）支持向量机分类器。我们的研究结果表明，在不同粗糙度水平的纹理之间，mu（8-15Hz）和 beta（16-30Hz）频带的总功率具有较高的鉴别精度（平均准确率>84%），但对运动类型（平均准确率<65%）和频率（平均准确率<58%）的分类贡献较低。
