Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4395-4398. doi: 10.1109/EMBC.2017.8037830.
Neuronal tuning property such as preferred direction and modulation depth could change gradually or abruptly in brain machine interface (BMI). The decoding performance will decay in static algorithms where dynamic neuronal tuning property is regarded as stationary. Many adaptive algorithms have been proposed to update the time-varying decoding parameter with main consideration on the decoding performance, but seldom focus on exploring how individual neuronal tuning property changes physiologically. We propose a novel adaptive algorithm based on sequential Monte Carlo point process estimation to capture the abrupt change of neuronal modulation depth and preferred direction. At each time point, the tuning parameter is assumed as static with a large probability and searched within a local area. Meanwhile, the abrupt change is thought to occur with a small probability and explored within a global range. This algorithm is tested on synthetic neural data and compared with a static point process algorithm. The results show that our adaptive algorithm succeeds in detecting the abrupt change in neuronal tuning, which contributes to a better reconstruction of kinematics.
在脑机接口（BMI）中，诸如偏好方向和调制深度等神经元调谐特性可能会逐渐或突然发生变化。在将动态神经元调谐特性视为固定不变的静态算法中，解码性能将会下降。已经提出了许多自适应算法来更新时变解码参数，主要考虑的是解码性能，但很少关注探索单个神经元调谐特性在生理上是如何变化的。我们提出了一种基于序贯蒙特卡罗点过程估计的新型自适应算法，以捕捉神经元调制深度和偏好方向的突然变化。在每个时间点，调谐参数大概率被假定为静态，并在局部区域内进行搜索。同时，认为突然变化以小概率发生，并在全局范围内进行探索。该算法在合成神经数据上进行了测试，并与静态点过程算法进行了比较。结果表明，我们的自适应算法成功地检测到了神经元调谐的突然变化，这有助于更好地重建运动学。
