Ofner Patrick, Müller-Putz Gernot R
IEEE Trans Biomed Eng. 2015 Mar;62(3):972-81. doi: 10.1109/TBME.2014.2377023. Epub 2014 Dec 4.
A brain-computer interface (BCI) can help to overcome movement deficits in persons with spinal-cord injury. Ideally, such a BCI detects detailed movement imaginations, i.e., trajectories, and transforms them into a control signal for a neuroprosthesis or a robotic arm restoring movement. Robotic arms have already been controlled successfully by means of invasive recording techniques, and executed movements have been reconstructed using noninvasive decoding techniques. However, it is unclear if detailed imagined movements can be decoded noninvasively using electroencephalography (EEG). We made progress toward imagined movement decoding and successfully classified horizontal and vertical imagined rhythmic movements of the right arm in healthy subjects using EEG. Notably, we used an experimental design which avoided muscle and eye movements to prevent classification results being affected. To classify imagined movements of the same limb, we decoded the movement trajectories and correlated them with assumed movement trajectories (horizontal and vertical). We then assigned the decoded movements to the assumed movements with the higher correlation. To train the decoder, we applied partial least squares, which allowed us to interpret the classifier weights although channels were highly correlated. To conclude, we showed the classification of imagined movements of one limb in two different movement planes in seven out of nine subjects. Furthermore, we found a strong involvement of the supplementary motor area. Finally, as our classifier was based on the decoding approach, we indirectly showed the decoding of imagined movements.
脑机接口（BCI）有助于克服脊髓损伤患者的运动功能障碍。理想情况下，这样的脑机接口能够检测详细的运动想象，即轨迹，并将其转化为用于神经假体或恢复运动的机器人手臂的控制信号。机器人手臂已经通过侵入性记录技术成功实现了控制，并且利用非侵入性解码技术重建了执行的运动。然而，尚不清楚是否可以使用脑电图（EEG）通过非侵入性方式解码详细的想象运动。我们在想象运动解码方面取得了进展，并利用脑电图成功地对健康受试者右臂的水平和垂直想象节律性运动进行了分类。值得注意的是，我们采用了一种避免肌肉和眼球运动的实验设计，以防止分类结果受到影响。为了对同一肢体的想象运动进行分类，我们对运动轨迹进行了解码，并将其与假定的运动轨迹（水平和垂直）进行关联。然后，我们将解码后的运动分配给相关性更高的假定运动。为了训练解码器，我们应用了偏最小二乘法，这使我们能够在通道高度相关的情况下解释分类器权重。总之，我们在九名受试者中的七名中展示了一个肢体在两个不同运动平面上的想象运动分类。此外，我们发现辅助运动区有强烈的参与。最后，由于我们的分类器基于解码方法，我们间接展示了想象运动的解码。
