Uejima Takeshi, Kita Kahori, Fujii Toshiyuki, Kato Ryu, Takita Masatoshi, Yokoi Hiroshi
Department of Precision Engineering, The University of Tokyo, Japan.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6469-72. doi: 10.1109/IEMBS.2009.5333547.
Brain-machine interfaces (BMIs) are expected to be used to assist seriously disabled persons' communications and reintegrate their motor functions. One of the difficult problems to realize practical BMI is how to record neural activity clearly and safely. Conventional invasive methods require electrodes inside the dura mater, and noninvasive methods do not involve surgery but have poor signal quality. Thus a low-invasive method of recording is important for safe and practical BMI. In this study, the authors used epidural electrodes placed between the skull and dura mater to record a rat's neural activity for low-invasive BMI. The signals were analyzed using a short-time Fourier transform, and the power spectra were classified into rat motions by a support vector machine. Classification accuracies were up to 96% in two-class discrimination, including that when the rat stopped, walked, and rested. The feasibility of a low-invasive BMI based on an epidural neural recording was shown in this study.
脑机接口（BMI）有望用于辅助严重残疾人士进行交流并恢复其运动功能。实现实用BMI的难题之一是如何清晰且安全地记录神经活动。传统的侵入性方法需要将电极置于硬脑膜内，而非侵入性方法虽无需手术，但信号质量较差。因此，一种低侵入性的记录方法对于安全且实用的BMI至关重要。在本研究中，作者使用置于颅骨和硬脑膜之间的硬膜外电极来记录大鼠的神经活动，以实现低侵入性BMI。使用短时傅里叶变换对信号进行分析，并通过支持向量机将功率谱按大鼠的运动进行分类。在包括大鼠停止、行走和休息的两类判别中，分类准确率高达96%。本研究展示了基于硬膜外神经记录的低侵入性BMI的可行性。
