Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
Interdepartmental Center for Research on Management and Innovation in Healthcare (CIRMIS), University of Naples Federico II, Naples, Italy.
Sci Rep. 2021 Mar 5;11(1):5297. doi: 10.1038/s41598-021-84447-8.
A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient's attention for enhancing the therapy effectiveness.
提出了一种基于脑电图（EEG）的运动康复任务中的分心检测方法。无线帽使用干电极和少量通道，保证了极高的佩戴舒适性。在来自 17 名志愿者的数据集上进行了实验验证。评估了来自空间、时间和频率域的不同特征提取和分类策略。比较了五种监督分类器在区分纯运动和分心时的注意力的性能。k 近邻分类器达到了 92.8±1.6%的准确率。在后一种情况下，特征提取基于自定义的 12 个通带滤波器组（FB）和公共空间模式（CSP）算法。特别是，分类的召回均值（分心检测中真阳性的百分比）高于 92%，允许治疗师或自动化系统知道何时刺激患者的注意力，以提高治疗效果。
