Sitaram Ranganatha, Zhang Haihong, Guan Cuntai, Thulasidas Manoj, Hoshi Yoko, Ishikawa Akihiro, Shimizu Koji, Birbaumer Niels
Institute for Infocomm Research, Singapore.
Neuroimage. 2007 Feb 15;34(4):1416-27. doi: 10.1016/j.neuroimage.2006.11.005. Epub 2006 Dec 28.
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.
作为一种供残疾人进行交流和环境控制的替代方式，脑机接口（BCI）技术的研究兴趣与日俱增，这些残疾人包括患有肌萎缩侧索硬化症（ALS）、脑干中风和脊髓损伤的患者。具备适当身体护理能力且有认知能力与社会环境进行交流的残疾患者能够在较长时间内保持合理的生活质量。近红外光谱技术是一种非侵入性技术，它利用近红外波段（700至1000纳米）的光来测定大脑局部区域的氧合作用、血流和代谢状态。在本文中，我们描述了一项旨在测试使用多通道近红外光谱技术开发脑机接口可行性的研究。我们使用一个连续波20通道近红外光谱系统，对5名健康志愿者的运动皮层进行测量，以检测他们在进行左手和右手运动想象时氧合血红蛋白和脱氧血红蛋白的变化。我们展示了信号分析结果，表明存在明显的血液动力学反应模式，可用于模式分类器以开发脑机接口。我们分别应用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，SVM区分左手想象和右手想象的平均准确率为73%，而HMM表现更佳，平均准确率为89%。我们的结果表明近红外光谱技术在脑机接口开发中具有潜在应用价值。我们还在此讨论了我们系统未来的扩展方向，即基于光标控制范式开发一个单词拼写应用程序，该范式结合了单次试验近红外光谱数据的在线模式分类。
