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脑机接口竞赛IV - 数据集I：用于基于自定进度脑电图的运动想象检测的学习判别模式

BCI Competition IV - Data Set I: Learning Discriminative Patterns for Self-Paced EEG-Based Motor Imagery Detection.

作者信息

Zhang Haihong, Guan Cuntai, Ang Kai Keng, Wang Chuanchu

机构信息

Institute for Infocomm Research, Agency for Science, Technology and Research Singapore.

出版信息

Front Neurosci. 2012 Feb 6;6:7. doi: 10.3389/fnins.2012.00007. eCollection 2012.

DOI:10.3389/fnins.2012.00007
PMID:22347153
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3272647/
Abstract

Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs), but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a non-linear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the BCI Competition IV on Dataset I where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set.

摘要

在脑信号中检测运动想象活动与非控制状态是自定节奏脑机接口（BCI）的基础，但由于运动想象以及非控制状态具有复杂和非平稳的特性，这也给信号处理带来了相当大的挑战。本文提出了一种基于稳健学习机制的自定节奏BCI，该机制提取并选择时空谱特征以区分多个脑电图类别。它还采用非线性回归和后处理技术，根据时空谱特征预测类别标签的时间序列。该方法在BCI竞赛IV的数据集I上得到了验证，在该数据集中它连续产生了最低的类别标签预测误差。本报告还展示并讨论了使用竞赛数据集对该方法的分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93b/3272647/d2a092f082cc/fnins-06-00007-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93b/3272647/ac5e94458bf4/fnins-06-00007-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93b/3272647/66f85728d625/fnins-06-00007-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93b/3272647/d2a092f082cc/fnins-06-00007-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93b/3272647/ac5e94458bf4/fnins-06-00007-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93b/3272647/66f85728d625/fnins-06-00007-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c93b/3272647/d2a092f082cc/fnins-06-00007-g003.jpg

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本文引用的文献

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Review of the BCI Competition IV.脑机接口竞赛IV综述。
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Classifying single-trial EEG during motor imagery by iterative spatio-spectral patterns learning (ISSPL).通过迭代时空谱模式学习（ISSPL）对运动想象期间的单次试验脑电图进行分类。
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