Intention estimation in brain-machine interfaces.
Intention estimation in brain-machine interfaces.
复制标题
脑机界面中的意图估计。
DOI：
10.1088/1741-2560/11/1/016004
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发表时间：
2014-02
影响因子：
4
通讯作者：
Shenoy KV
中科院分区：
文献类型：
--
作者：
Fan JM;Nuyujukian P;Kao JC;Chestek CA;Ryu SI;Shenoy KV
这项工作的目的是定量地研究最近报道的“重新校准的反馈意图训练卡尔曼滤波”(REFIT-KF)的性能提高的机制。这是通过设计REFIT-KF算法的变体并评估训练和在线数据来理解这一改进的神经基础来实现的。我们重点评估了REFIT-KF算法的两个训练集创新：意图估计和两阶段训练范式的贡献。在两阶段训练范式中，我们发现意图估计独立地提高了植入多单位皮质内阵列的两只猴子的目标获得率分别为37%和59%。意图估计通过增强运动学和神经训练数据之间的调谐特性和互信息来提高性能。此外，意图估计导致训练集和在线控制之间的通道调谐变化较少，这表明在线控制需要较少的适应。用在线BMI训练数据重新训练解码者也减少了调谐的变化，这表明在相同的行为背景下训练解码者是有好处的；然而，重新训练也会导致较慢的在线解码速度。最后，我们证明了当使用意图估计时，一阶段训练范例和两阶段训练范例的表现相当。这些发现突出了意图估计在减少适应性策略的需要和提高BMI的在线性能方面的效用，有助于指导未来的BMI设计决策。
The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported ‘recalibrated feedback intention-trained Kalman Filter’ (ReFIT-KF). This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm. Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied. These findings highlight the utility of intention estimation in reducing the need of adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.
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影响因子：
64.8
作者：
Ethier, C.;Oby, E. R.;Bauman, M. J.;Miller, L. E.
通讯作者：
Miller, L. E.
影响因子：
4
作者：
Chestek CA;Gilja V;Nuyujukian P;Foster JD;Fan JM;Kaufman MT;Churchland MM;Rivera-Alvidrez Z;Cunningham JP;Ryu SI;Shenoy KV
通讯作者：
Shenoy KV
偏差，最佳线性估计以及基于峰值的大脑计算机界面算法的开环模拟与闭环性能之间的差异。
DOI：
10.1016/j.neunet.2009.05.005
发表时间：
2009-11
期刊：
Neural networks : the official journal of the International Neural Network Society
影响因子：
--
作者：
Chase SM;Schwartz AB;Kass RE
通讯作者：
Kass RE
影响因子：
24.8
作者：
Andersen RA;Hwang EJ;Mulliken GH
通讯作者：
Mulliken GH
影响因子：
13.9
作者：
Hatsopoulos NG;Donoghue JP
通讯作者：
Donoghue JP