Grenoble Universities, Saint Martin d'Hères, France.
Brain Topogr. 2012 Jan;25(1):55-63. doi: 10.1007/s10548-011-0193-y. Epub 2011 Jul 10.
A challenge in designing a Brain-Computer Interface (BCI) is the choice of the channels, e.g. the most relevant sensors. Although a setup with many sensors can be more efficient for the detection of Event-Related Potential (ERP) like the P300, it is relevant to consider only a low number of sensors for a commercial or clinical BCI application. Indeed, a reduced number of sensors can naturally increase the user comfort by reducing the time required for the installation of the EEG (electroencephalogram) cap and can decrease the price of the device. In this study, the influence of spatial filtering during the process of sensor selection is addressed. Two of them maximize the Signal to Signal-plus-Noise Ratio (SSNR) for the different sensor subsets while the third one maximizes the differences between the averaged P300 waveform and the non P300 waveform. We show that the locations of the most relevant sensors subsets for the detection of the P300 are highly dependent on the use of spatial filtering. Applied on data from 20 healthy subjects, this study proves that subsets obtained where sensors are suppressed in relation to their individual SSNR are less efficient than when sensors are suppressed in relation to their contribution once the different selected sensors are combined for enhancing the signal. In other words, it highlights the difference between estimating the P300 projection on the scalp and evaluating the more efficient sensor subsets for a P300-BCI. Finally, this study explores the issue of channel commonality across subjects. The results support the conclusion that spatial filters during the sensor selection procedure allow selecting better sensors for a visual P300 Brain-Computer Interface.
设计脑机接口（BCI）的一个挑战是通道的选择，例如最相关的传感器。虽然使用许多传感器的设置对于检测事件相关电位（ERP）如 P300 可能更有效，但对于商业或临床 BCI 应用，考虑使用少数传感器也是很重要的。事实上，减少传感器的数量可以通过减少 EEG（脑电图）帽的安装时间自然地增加用户的舒适度，并降低设备的价格。在这项研究中，研究了在传感器选择过程中进行空间滤波的影响。其中两种方法最大化了不同传感器子集的信号与信号加噪声比（SSNR），而第三种方法则最大化了平均 P300 波形和非 P300 波形之间的差异。我们表明，用于检测 P300 的最相关传感器子集的位置高度依赖于空间滤波的使用。应用于 20 名健康受试者的数据，本研究证明了，与根据其个体 SSNR 抑制传感器的子集相比，当根据它们对不同选择的传感器的组合为信号增强的贡献来抑制传感器时，获得的子集效率较低。换句话说，它突出了估计头皮上 P300 投影与评估用于 P300-BCI 的更有效的传感器子集之间的区别。最后，本研究探讨了通道在受试者之间的共同性问题。结果支持了这样的结论，即在传感器选择过程中使用空间滤波器可以为视觉 P300 脑机接口选择更好的传感器。
