Achtman Neil, Afshar Afsheen, Santhanam Gopal, Yu Byron M, Ryu Stephen I, Shenoy Krishna V
Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
J Neural Eng. 2007 Sep;4(3):336-47. doi: 10.1088/1741-2560/4/3/018. Epub 2007 Aug 22.
Neural prostheses aim to improve the quality of life of severely disabled patients by translating neural activity into control signals for guiding prosthetic devices or computer cursors. We recently demonstrated that plan activity from premotor cortex, which specifies the endpoint of the upcoming arm movement, can be used to swiftly and accurately guide computer cursors to the desired target locations. However, these systems currently require additional, non-neural information to specify when plan activity is present. We report here the design and performance of state estimator algorithms for automatically detecting the presence of plan activity using neural activity alone. Prosthesis performance was nearly as good when state estimation was used as when perfect plan timing information was provided separately ( approximately 5 percentage points lower, when using 200 ms of plan activity). These results strongly suggest that a completely neurally-driven high-performance brain-computer interface is possible.
神经假体旨在通过将神经活动转化为控制信号来指导假肢装置或计算机光标，从而改善严重残疾患者的生活质量。我们最近证明，运动前皮层的计划活动（它指定即将到来的手臂运动的终点）可用于快速准确地将计算机光标引导到所需的目标位置。然而，这些系统目前需要额外的非神经信息来确定计划活动何时出现。我们在此报告状态估计器算法的设计和性能，该算法仅使用神经活动就能自动检测计划活动的存在。当使用状态估计时，假肢性能与单独提供完美计划时间信息时几乎一样好（使用200毫秒计划活动时，性能低约5个百分点）。这些结果有力地表明，完全由神经驱动的高性能脑机接口是可能的。
