Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface
Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface
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DOI：
10.1016/j.neuroimage.2006.11.005
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发表时间：
2007-02-15
期刊：
影响因子：
5.7
通讯作者：
Birbaumer, Niels
中科院分区：
文献类型：
--
作者：
Sitaram, Ranganatha;Zhang, Haihong;Birbaumer, Niels
脑机接口（BCI）技术作为一种可供残疾人（如患有肌萎缩侧索硬化症（ALS）、脑干中风和脊髓损伤的患者）使用的替代通信和环境控制模式，其研究兴趣日益增加。身体得到适当护理且具备与社会环境交流的认知能力的残疾患者能够在较长时间内保持合理的生活质量。近红外光谱是一种非侵入性技术，它利用近红外范围（700至1000纳米）的光来确定大脑局部区域的脑氧合、血流和代谢状态。在本文中，我们描述了一项旨在测试在开发脑机接口中使用多通道近红外光谱技术可行性的研究。我们在5名健康志愿者的运动皮层上使用了连续波20通道近红外光谱系统，以测量在左手和右手运动想象过程中氧合血红蛋白和去氧血红蛋白的变化。我们给出的信号分析结果表明，存在明显的血流动力学反应模式，这些模式可在模式分类器中用于开发脑机接口。我们分别应用了两种不同的模式识别算法，支持向量机（SVM）和隐马尔可夫模型（HMM），对数据进行离线分类。对于所有志愿者，支持向量机区分左手想象和右手想象的平均准确率为73%，而隐马尔可夫模型表现更好，平均准确率为89%。我们的研究结果表明近红外光谱在脑机接口开发中具有潜在应用。我们在此还讨论了我们系统未来的扩展，即基于光标控制范式并结合单次试验近红外光谱数据的在线模式分类来开发一个单词拼写应用程序。（c）2006爱思唯尔公司。保留所有权利。
There has been an increase in research interest for brain-computer interface (BCI) technology as an alternate mode of communication and environmental control for the disabled, such as patients suffering from amyotrophic lateral sclerosis (ALS), brainstem stroke and spinal cord injury. Disabled patients with appropriate physical care and cognitive ability to communicate with their social environment continue to live with a reasonable quality of life over extended periods of time. Near-infrared spectroscopy is a non-invasive technique which utilizes light in the near-infrared range (700 to 1000 nm) to determine cerebral oxygenation, blood flow and metabolic status of localized regions of the brain. In this paper, we describe a study conducted to test the feasibility of using multichannel NIRS in the development of a BCI. We used a continuous wave 20-channel NIRS system over the motor cortex of 5 healthy volunteers to measure oxygenated and deoxygenated hemoglobin changes during left-hand and right-hand motor imagery. We present results of signal analysis indicating that there exist distinct patterns of hemodynamic responses which could be utilized in a pattern classifier towards developing a BCI. We applied two different pattern recognition algorithms separately, Support Vector Machines (SVM) and Hidden Markov Model (HMM), to classify the data offline. SVM classified left-hand imagery from right-hand imagery with an average accuracy of 73 % for all volunteers, while HMM performed better with an average accuracy of 89%. Our results indicate potential application of NIRS in the development of BCIs. We also discuss here future extension of our system to develop a word speller application based on a cursor control paradigm incorporating online pattern classification of single-trial NIRS data. (c) 2006 Elsevier Inc. All rights reserved.