Liang Nanying, Bougrain Laurent
Inria, Villers-lès-Nancy F-54600, France.
Front Neurosci. 2012 Jun 28;6:91. doi: 10.3389/fnins.2012.00091. eCollection 2012.
This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV.
本文介绍了在第四届脑机接口（BCI）竞赛中获胜的方法，该方法用于根据皮层脑电图（ECoG）信号预测手指弯曲。基于ECoG的脑机接口最近引起了该领域的关注。事实上，与传统脑电图记录相比，ECoG能够提供更高的空间分辨率和更好的信号质量。它也更适合长期使用。这些特性有助于解码精确的大脑活动，并实现高效的基于ECoG的神经假体。在脑机接口研究中，将大脑信号转化为指令，信号处理是一项非常重要的任务。在此，我们提出一种基于特定频段ECoG幅度调制的线性回归方法，该方法包含用于个体手指弯曲预测的短期记忆。在第四届BCI竞赛的数据集4上，通过预测值与记录的手指弯曲值之间的相关系数达到最高值，证明了该方法的有效性。
