Objective. Magnetoencephalography (MEG) based brain–computer interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels (NoC) means an increase in computational complexity. However, not all sensors necessarily contribute significantly to an increase in classification accuracy (CA) and specifically in the case of MEG-based BCI no channel selection methodology has been performed. Therefore, this study investigates the effect of channel selection on the performance of MEG-based BCI. Approach. MEG data were recorded for two sessions from 15 healthy participants performing motor imagery, cognitive imagery and a mixed imagery task pair using a unique paradigm. Performance of four state-of-the-art channel selection methods (i.e. Class-Correlation, ReliefF, Random Forest, and Infinit...

中文翻译：

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评估通道选择对 MEG 数据中运动和认知图像解码的影响

客观的。基于脑磁图 (MEG) 的脑机接口 (BCI) 涉及大量传感器，可提供更好的时空分辨率来评估大脑活动模式。尽管通道数量 (NoC) 的增加意味着计算复杂性的增加，但使用 MEG 开发具有高精度的 BCI 已经进行了许多努力。然而，并非所有传感器都必然对提高分类精度 (CA) 有显着贡献，特别是在基于 MEG 的 BCI 的情况下，没有执行通道选择方法。因此，本研究调查了通道选择对基于 MEG 的 BCI 性能的影响。方法。记录了 15 名健康参与者的两个会话的 MEG 数据，这些参与者使用独特的范式执行运动想象、认知想象和混合想象任务对。