Temmar Hisham, Willsey Matthew S, Costello Joseph T, Mender Matthew J, Cubillos Luis Hernan, DeMatteo Jesse C, Lam Jordan Lw, Wallace Dylan M, Kelberman Madison M, Patil Parag G, Chestek Cynthia A
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.
Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, United States of America.
J Neural Eng. 2025 Jun 27;22(3):036050. doi: 10.1088/1741-2552/ade568.
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 nonlinear and linear approaches predict individuated finger movements in open and closed-loop settings.Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex and performed a 2D dexterous finger movement task for a juice reward. Multiple linear and nonlinear 'decoders' were used to map from recorded spiking band power into movement kinematics. Performance of these decoders was compared and analyzed to determine how nonlinear decoders perform in both open and closed-loop scenarios.We show that nonlinear decoders enable control which more closely resembles true hand movements, producing distributions of velocities 80.7% 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 temporally-convolved feedforward neural network convergence by up to 188.9%, along with improving average performance and training speed. Finally, we show that TCNs and long short-term memory can effectively leverage training data from multiple task variations to improve generalization.The results of this study support artificial neural networks of all kinds as the future of BMI decoding and show potential for generalizing over less constrained tasks.
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解码器的性能已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的改进。在本研究中，我们比较了非线性和线性方法在开环和闭环设置中预测个体化手指运动的方式。两只成年雄性恒河猴在运动皮层植入了犹他阵列，并执行二维灵巧手指运动任务以获取果汁奖励。使用多个线性和非线性“解码器”将记录的尖峰带功率映射为运动学。对这些解码器的性能进行比较和分析，以确定非线性解码器在开环和闭环场景中的表现。我们表明，非线性解码器能够实现更接近真实手部运动的控制，其产生的速度分布比线性解码器更接近真实手部控制，相似度高达80.7%。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可将时间卷积前馈神经网络收敛的一致性提高多达188.9%，同时提高平均性能和训练速度。最后，我们表明门控循环单元（TCN）和长短期记忆网络（LSTM）可以有效地利用来自多个任务变体的训练数据来提高泛化能力。本研究结果支持将各类人工神经网络作为BMI解码的未来发展方向，并显示出在约束较少的任务中进行泛化的潜力。
