Foldes S T, Vinjamuri R K, Wang W, Weber D J, Collinger J L
Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5778-81. doi: 10.1109/IEMBS.2011.6091430.
Movement-related field potentials can be extracted and processed in real-time with magnetoencephalography (MEG) and used for brain machine interfacing (BMI). However, due to its immense sensitivity to magnetic fields, MEG is prone to a low signal to noise ratio. It is therefore important to collect enough initial data to appropriately characterize motor-related activity and to ensure that decoders can be built to adequately translate brain activity into BMI-device commands. This is of particular importance for therapeutic BMI applications where less time spent collecting initial open-loop data means more time for performing neurofeedback training which could potentially promote cortical plasticity and rehabilitation. This study evaluated the amount of hand-grasp movement and rest data needed to characterize sensorimotor modulation depth and build classifier functions to decode brain states in real-time. It was determined that with only five minutes of initial open-loop MEG data, decoders can be built to classify brain activity as grasp or rest in real-time with an accuracy of 84 ± 6%.
与运动相关的场电位可以通过脑磁图（MEG）实时提取和处理，并用于脑机接口（BMI）。然而，由于MEG对磁场极其敏感，其信噪比很低。因此，收集足够的初始数据以适当地表征与运动相关的活动，并确保能够构建解码器以将大脑活动充分转化为BMI设备命令非常重要。这对于治疗性BMI应用尤为重要，因为收集初始开环数据所花费的时间越少，进行神经反馈训练的时间就越多，这可能会促进皮质可塑性和康复。本研究评估了表征感觉运动调制深度和构建分类器函数以实时解码脑状态所需的手部抓握运动和静息数据量。结果表明，仅需五分钟的初始开环MEG数据，就可以构建解码器，以84±6%的准确率实时将大脑活动分类为抓握或静息。
