Rivet Bertrand, Cecotti Hubert, Phlypo Ronald, Bertrand Olivier, Maby Emmanuel, Mattout Jeremie
GIPSAlab, CNRS-UMR 5216, Grenoble Institute of Technology, France.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5379-82. doi: 10.1109/IEMBS.2010.5626485.
A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by direct control from decoding of brain activity. This paper deals with the P300-speller application that enables to write a text based on the oddball paradigm. To improve the ergonomics and minimize the cost of such a BCI, reducing the number of electrodes is mandatory. We propose a new algorithm to select a relevant subset of electrodes by estimating sparse spatial filters. A l(1)-norm penalization term, as an approximation of the l(0)-norm, is introduced in the xDAWN algorithm, which maximizes the signal to signal-plus-noise ratio. Experimental results on 20 subjects show that the proposed method is efficient to select the most relevant sensors: from 32 down to 10 sensors, the loss in classification accuracy is less than 5%.
脑机接口（BCI）是一种特定类型的人机接口，它通过对大脑活动进行解码来实现直接控制，从而使受试者/患者与计算机之间进行通信。本文探讨了基于异常球范式实现文本书写的P300拼写器应用。为了改善此类脑机接口的人体工程学设计并降低成本，减少电极数量是必不可少的。我们提出了一种新算法，通过估计稀疏空间滤波器来选择相关的电极子集。在xDAWN算法中引入了l(1)范数惩罚项，作为l(0)范数的近似，该算法可使信号与信号加噪声比最大化。对20名受试者的实验结果表明，该方法能够有效地选择最相关的传感器：从32个传感器减少到10个传感器时，分类准确率的损失小于5%。
