Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16/IV, 8010 Graz, Austria.
J Neural Eng. 2018 Feb;15(1):016005. doi: 10.1088/1741-2552/aa8911.
Despite the high number of degrees of freedom of the human hand, most actions of daily life can be executed incorporating only palmar, pincer and lateral grasp. In this study we attempt to discriminate these three different executed reach-and-grasp actions utilizing their EEG neural correlates.
In a cue-guided experiment, 15 healthy individuals were asked to perform these actions using daily life objects. We recorded 72 trials for each reach-and-grasp condition and from a no-movement condition.
Using low-frequency time domain features from 0.3 to 3 Hz, we achieved binary classification accuracies of 72.4%, STD ± 5.8% between grasp types, for grasps versus no-movement condition peak performances of 93.5%, STD ± 4.6% could be reached. In an offline multiclass classification scenario which incorporated not only all reach-and-grasp actions but also the no-movement condition, the highest performance could be reached using a window of 1000 ms for feature extraction. Classification performance peaked at 65.9%, STD ± 8.1%. Underlying neural correlates of the reach-and-grasp actions, investigated over the primary motor cortex, showed significant differences starting from approximately 800 ms to 1200 ms after the movement onset which is also the same time frame where classification performance reached its maximum.
We could show that it is possible to discriminate three executed reach-and-grasp actions prominent in people's everyday use from non-invasive EEG. Underlying neural correlates showed significant differences between all tested conditions. These findings will eventually contribute to our attempt of controlling a neuroprosthesis in a natural and intuitive way, which could ultimately benefit motor impaired end users in their daily life actions.
尽管人手有大量自由度，但日常生活中的大多数动作都可以通过手掌、钳子和侧抓来完成。在这项研究中，我们试图利用其 EEG 神经相关性来区分这三种不同的执行抓握动作。
在提示引导的实验中，15 名健康个体被要求使用日常生活物品来执行这些动作。我们为每个伸手抓握条件和无运动条件记录了 72 次试验。
使用 0.3 到 3 Hz 的低频时域特征，我们在抓握类型之间达到了 72.4%的二进制分类准确率，STD±5.8%，在抓握与无运动条件的峰值表现中，我们达到了 93.5%的准确率，STD±4.6%。在包含所有伸手抓握动作以及无运动条件的离线多类分类场景中，使用 1000ms 的特征提取窗口可以达到最高性能。分类性能达到 65.9%，STD±8.1%。在运动开始后大约 800ms 到 1200ms 之间，研究原发性运动皮层的伸手抓握动作的潜在神经相关性显示出显著差异，这也是分类性能达到最大值的相同时间框架。
我们表明，使用非侵入性 EEG 可以区分人们日常使用的三种执行的伸手抓握动作。潜在的神经相关性显示出所有测试条件之间的显著差异。这些发现最终将有助于我们尝试以自然和直观的方式控制神经假肢，这最终将使运动受损的最终用户受益于他们的日常生活动作。
