Validation of a non-invasive, real-time, human-in-the-loop model of intracortical brain-computer interfaces.
Validation of a non-invasive, real-time, human-in-the-loop model of intracortical brain-computer interfaces.
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DOI：
10.1088/1741-2552/ac97c3
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发表时间：
2022-10-18
影响因子：
4
通讯作者：
Danziger, Zachary C.
中科院分区：
文献类型：
--
作者：
Awasthi, Peeyush;Lin, Tzu-Hsiang;Bae, Jihye;Miller, Lee E.;Danziger, Zachary C.
目标。尽管侵入性脑机接口（ibci）有着巨大的前景，但相关的研究成本、风险和伦理考虑限制了开发和测试将神经活动解码为用户意图的算法的机会。我们的目标是通过设计一个能够在闭环中测试许多人类受试者的iBCI模型来解决这一挑战。的方法。我们开发了一个iBCI模型，该模型使用人工神经网络（ann）将人类手指运动转化为真实的运动皮层放电模式，然后可以实时解码。我们称这个模型为关节角BCI或jaBCI。jaBCI允许随时招募的健康受试者使用任何神经解码器执行闭环iBCI任务，保留受试者与控制相关的短延迟错误纠正和学习动态。主要的结果。我们通过模拟神经元放电统计离线验证了jaBCI，证实模拟神经信号具有放电率、低维PCA几何形状和旋转jPCA动态，这些与我们训练神经网络的实际神经元（记录在猴子M1中）非常相似。我们还在闭环实验中测试了jaBCI，我们的单一研究检查了与全球ibci测试人数大致相同的受试者（n = 25）。其表现与植入皮质内电极的瘫痪iBCI使用者一致。jaBCI允许我们模仿实验协议（例如，相同速度的卡尔曼滤波解码器和中心输出任务），并计算在三个关键研究中使用的相同的七个行为测量。的意义。这些令人鼓舞的结果表明，jaBCI的实时发射速率仿真是一种有用的手段，可以为解码算法的快速原型设计和优化、iBCI中双向学习的研究以及改进iBCI控制提供统计上稳健的样本量。
Objective. Despite the tremendous promise of invasive brain-computer interfaces (iBCIs), the associated study costs, risks, and ethical considerations limit the opportunity to develop and test the algorithms that decode neural activity into a user’s intentions. Our goal was to address this challenge by designing an iBCI model capable of testing many human subjects in closed-loop. Approach. We developed an iBCI model that uses artificial neural networks (ANNs) to translate human finger movements into realistic motor cortex firing patterns, which can then be decoded in real time. We call the model the joint angle BCI, or jaBCI. jaBCI allows readily recruited, healthy subjects to perform closed-loop iBCI tasks using any neural decoder, preserving subjects’ control-relevant short-latency error correction and learning dynamics. Main results. We validated jaBCI offline through emulated neuron firing statistics, confirming that emulated neural signals have firing rates, low-dimensional PCA geometry, and rotational jPCA dynamics that are quite similar to the actual neurons (recorded in monkey M1) on which we trained the ANN. We also tested jaBCI in closed-loop experiments, our single study examining roughly as many subjects as have been tested world-wide with iBCIs (n = 25). Performance was consistent with that of the paralyzed, human iBCI users with implanted intracortical electrodes. jaBCI allowed us to imitate the experimental protocols (e.g. the same velocity Kalman filter decoder and center-out task) and compute the same seven behavioral measures used in three critical studies. Significance. These encouraging results suggest the jaBCI’s real-time firing rate emulation is a useful means to provide statistically robust sample sizes for rapid prototyping and optimization of decoding algorithms, the study of bi-directional learning in iBCIs, and improving iBCI control.
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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.
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：
17.1
作者：
Jarosiewicz B;Sarma AA;Bacher D;Masse NY;Simeral JD;Sorice B;Oakley EM;Blabe C;Pandarinath C;Gilja V;Cash SS;Eskandar EN;Friehs G;Henderson JM;Shenoy KV;Donoghue JP;Hochberg LR
通讯作者：
Hochberg LR
DOI：
10.1073/pnas.0808113105
发表时间：
2008-12-09
影响因子：
11.1
作者：
Jarosiewicz, Beata;Chase, Steven M.;Schwartz, Andrew B.
通讯作者：
Schwartz, Andrew B.
对于四肢瘫痪患者，在心脏内脑机构界面中闭环校准的优点。
影响因子：
4
作者：
Jarosiewicz B;Masse NY;Bacher D;Cash SS;Eskandar E;Friehs G;Donoghue JP;Hochberg LR
通讯作者：
Hochberg LR