Temmar Hisham, Willsey Matthew S, Costello Joseph T, Mender Matthew J, Cubillos Luis H, Lam Jordan Lw, Wallace Dylan M, Kelberman Madison M, Patil Parag G, Chestek Cynthia A
bioRxiv. 2024 Mar 5:2024.03.01.583000. doi: 10.1101/2024.03.01.583000.
Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks.
A neural network decoder produces consistent naturalistic movements and shows potential for real-world generalization through task variations.
脑机接口（BMI）旨在通过将神经信号“解码”为行为，来恢复脊髓损伤患者的功能。最近，非线性BMI解码器的性能超过了先前的线性解码器，但很少有研究探讨这些非线性方法带来了哪些具体改进。在本研究中，我们比较了时间卷积前馈神经网络（tcFNN）和线性方法在开环和闭环设置中如何预测个体化手指运动。我们发现，非线性解码器能产生更自然的运动，其速度分布与真实手部控制的接近程度比线性解码器高85.3%。针对神经网络可能得出不一致解决方案的担忧，我们发现正则化技术将tcFNN收敛的一致性提高了194.6%，同时提高了平均性能和训练速度。最后，我们表明tcFNN可以利用来自多个任务变体的训练数据来提高泛化能力。本研究结果表明，非线性方法能产生更自然的运动，并显示出在约束较少的任务上进行泛化的潜力。
神经网络解码器能产生一致的自然运动，并显示出通过任务变体进行现实世界泛化的潜力。
