Efficient decoding with steady-state Kalman filter in neural interface systems.
Efficient decoding with steady-state Kalman filter in neural interface systems.
复制标题
DOI：
10.1109/tnsre.2010.2092443
复制
发表时间：
2011-02
期刊：
影响因子：
--
通讯作者：
Hochberg LR
中科院分区：
文献类型：
--
作者：
Malik WQ;Truccolo W;Brown EN;Hochberg LR
卡尔曼滤波器通常用于神经接口系统中，以解码神经活动并估计所需的运动学。我们分析了一个低复杂度的卡尔曼滤波器的实现，其中滤波器增益近似其稳态形式，计算离线实时解码开始之前。我们评估其性能，使用人类运动皮层尖峰序列数据从一个皮层内记录阵列的一部分，正在进行的试点临床试验。我们证明了标准卡尔曼滤波器增益在1.5 ± 0.5 s（平均值± s.d.）内收敛到稳态滤波器增益的95%以内。由两个滤波器解码的预期运动速度的差异在5秒内消失，在会话长度上两个解码速度之间的相关系数为0.99。我们还发现，稳态卡尔曼滤波器将解码25 ± 3个单个单元的发射率的计算负载（算法执行时间）减少了7.0 ± 0.9倍。我们预计，在具有较大神经集成的系统中，计算效率的增益将高得多。因此，稳态滤波器可以在估计精度方面以很小的成本提供相当大的运行时间效率。这种更有效的神经解码方法将促进未来大规模、多信号神经接口系统的实际实现。
The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5 ± 0.5 s (mean ± s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25 ± 3 single units by a factor of 7.0 ± 0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.