Wang Y, Truccolo W, Borton D A
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1944-1947. doi: 10.1109/EMBC.2018.8512609.
Recent machine learning techniques have become a powerful tool in a variety of tasks, including neural decoding. Deep neural networks, particularly recurrent models, leverage the temporal evolution of neural ensemble activity to decode complex movement and sensory signals. Using single-unit recordings from microelectrode arrays implanted in the leg area of primary motor cortex in non-human primates, we decode the positions and angles of hindlimb joints during a locomotion task using a long short-term memory (LSTM) network. The LSTM decoder improved decoding over traditional filtering methods, such as Wiener and Kalman filters. However, dramatic improvements over other machine learning (e.g. XGBoost) and latent state-space methods were not observed.
最近，机器学习技术已成为包括神经解码在内的各种任务中的强大工具。深度神经网络，特别是循环模型，利用神经集合活动的时间演变来解码复杂的运动和感觉信号。我们使用植入非人类灵长类动物初级运动皮层腿部区域的微电极阵列进行单单元记录，通过长短期记忆（LSTM）网络对运动任务期间后肢关节的位置和角度进行解码。与传统滤波方法（如维纳滤波器和卡尔曼滤波器）相比，LSTM解码器改善了解码效果。然而，未观察到相对于其他机器学习方法（如XGBoost）和潜在状态空间方法的显著改进。
