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Multi-channel linear descriptors for event-related EEG collected in brain computer interface.用于脑机接口中采集的事件相关脑电图的多通道线性描述符。
J Neural Eng. 2006 Mar;3(1):52-8. doi: 10.1088/1741-2560/3/1/006. Epub 2006 Feb 6.
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Towards adaptive classification for BCI.面向脑机接口的自适应分类
J Neural Eng. 2006 Mar;3(1):R13-23. doi: 10.1088/1741-2560/3/1/R02. Epub 2006 Mar 1.
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Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks.μ节律去同步化与不同运动想象任务的脑电图单试次分类
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Decreased nonlinear complexity and chaos during sleep in first episode schizophrenia: a preliminary report.
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IEEE Trans Biomed Eng. 2004 Jun;51(6):1044-51. doi: 10.1109/TBME.2004.826692.
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Mu rhythm-based cursor control: an offline analysis.基于缪节律的光标控制：离线分析
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新型脑机接口技术特点

Novel features for brain-computer interfaces.

机构信息

Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.

出版信息

Comput Intell Neurosci. 2007;2007:82827. doi: 10.1155/2007/82827.

DOI:10.1155/2007/82827
PMID:18364991
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2267903/
Abstract

While conventional approaches of BCI feature extraction are based on the power spectrum, we have tried using nonlinear features for classifying BCI data. In this paper, we report our test results and findings, which indicate that the proposed method is a potentially useful addition to current feature extraction techniques.

摘要

虽然传统的脑机接口特征提取方法基于功率谱，但我们已经尝试使用非线性特征来对脑机接口数据进行分类。在本文中，我们报告了我们的测试结果和发现，这表明所提出的方法是当前特征提取技术的一个潜在有用的补充。