Abstract：
Objectives. Parkinson patients often suffer from motor impairments such as tremor and freezing of movement that can be difficult to treat. To unfreeze movement, it has been suggested to provide sensory stimuli. To avoid constant stimulation, episodes with freezing of movement needs to be detected which is a challenge. This can potentially be obtained using a brain-computer interface (BCI) based on movement-related cortical potentials (MRCPs) that are observed in association with the intention to move. The objective in this study was to detect MRCPs from single-trial EEG.Approach. Nine Parkinson patients executed 100 wrist movements and 100 ankle movements while continuous EEG and EMG were recorded. The experiment was repeated in two sessions on separate days. Using temporal, spectral and template matching features, a random forest (RF), linear discriminant analysis, and k-nearest neighbours (kNN) classifier were constructed in offline analysis to discriminate between epochs containing movement-related or idle brain activity to provide an estimation of the performance of a BCI. Three classification scenarios were tested: 1) within-session (using training and testing data from the same session and participant), between-session (using data from the same participant from session one for training and session two for testing), and across-participant (using data from all participants except one for training and testing on the remaining participant).Main results. The within-session classification scenario was associated with the highest classification accuracies which were in the range of 88%-89% with a similar performance across sessions. The performance dropped to 69%-75% and 70%-75% for the between-session and across-participant classification scenario, respectively. The highest classification accuracies were obtained for the RF and kNN classifiers.Significance. The results indicate that it is possible to detect movement intentions in individuals with Parkinson\'s disease such that they can operate a BCI which may control the delivery of sensory stimuli to unfreeze movement.
摘要：
目的：帕金森病患者经常患有运动障碍，如震颤和运动冻结，这可能很难治疗。要解冻运动，有人建议提供感官刺激。为了避免持续的刺激，需要检测运动冻结的情节，这是一个挑战。这可以使用脑机接口(BCI)基于与移动意图相关的运动相关的皮层电位(MRCP)来获得。这项研究的目的是从单次试验脑电图中检测MRCP。＆＃xD;方法：九名帕金森氏症患者在记录连续脑电图和肌电图的同时，进行了100次腕关节运动和100次踝关节运动。在不同的日子分两次重复该实验。使用temporal，光谱和模板匹配功能，随机森林，线性判别分析，在离线分析中构建了k个最近邻分类器，以区分包含运动相关或空闲大脑活动的时期，以提供对BCI性能的估计。测试了三种分类方案：1)会话内(使用来自同一会话和参与者的训练和测试数据)，会话之间(使用来自会话一的同一参与者的数据进行培训，使用会话二的数据进行测试)，和跨参与者(使用来自所有参与者的数据，除了一个用于培训和测试其余参与者)。
主要结果：会话内分类方案与最高分类精度相关，在88-89%的范围内，不同会话的性能相似。会话间和跨参与者分类方案的性能下降到69-75%和70-75%，分别。对于随机森林和k近邻分类器获得最高的分类精度。＆＃xD;意义：结果表明，可以检测帕金森病患者的运动意图，以便他们可以操作BCI，该BCI可以控制感官刺激的传递以解冻运动。
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