Detection and classification of movement-related cortical potentials associated with task force and speed
Detection and classification of movement-related cortical potentials associated with task force and speed
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DOI：
10.1088/1741-2560/10/5/056015
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发表时间：
2013-10-01
影响因子：
4
通讯作者：
Dremstrup, Kim
中科院分区：
文献类型：
--
作者：
Jochumsen, Mads;Niazi, Imran Khan;Dremstrup, Kim
Objective.在这项研究中，目的是检测运动意图和提取不同水平的力量和速度的预期运动从头皮脑电图（EEG）。然后，我们估计了闭环系统的性能。Approach.线索运动检测连续脑电图记录使用模板的运动相关的皮层电位在12名健康受试者的初始阶段。从运动意图中提取时间特征，利用优化的支持向量机进行分类。当检测与分类相结合时，系统性能进行了评估。主要结果。该系统检测到81%的运动，并正确分类75 +/- 9%和80 +/- 10%的这些在检测点时，改变力和速度，分别。当检测器与分类器相结合时，系统检测并正确分类了64 +/- 13%和67 +/- 13%的这些运动。该系统检测并错误分类了21 +/- 7%和16 +/- 9%的运动。在运动开始前317 +/- 73 ms检测到运动。意义研究结果表明，它是可能的，以有限的lavery检测运动意图，并提取和分类不同级别的力量和速度，这可能是与辅助技术相结合，病人驱动的神经康复。
Objective. In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system. Approach. Cued movements were detected from continuous EEG recordings using a template of the initial phase of the movement-related cortical potential in 12 healthy subjects. The temporal features, extracted from the movement intention, were classified with an optimized support vector machine. The system performance was evaluated when combining detection with classification. Main results. The system detected 81% of the movements and correctly classified 75 +/- 9% and 80 +/- 10% of these at the point of detection when varying the force and speed, respectively. When the detector was combined with the classifier, the system detected and correctly classified 64 +/- 13% and 67 +/- 13% of these movements. The system detected and incorrectly classified 21 +/- 7% and 16 +/- 9% of the movements. The movements were detected 317 +/- 73 ms before the movement onset. Significance. The results indicate that it is possible to detect movement intentions with limited latencies, and extract and classify different levels of force and speed, which may be combined with assistive technologies for patient-driven neurorehabilitation.