End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network
End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network
复制标题
DOI：
10.1109/biocas.2019.8919131
复制
发表时间：
2019-10
期刊：
影响因子：
--
通讯作者：
Nur Ahmadi;T. Constandinou;C. Bouganis
中科院分区：
文献类型：
--
作者：
Nur Ahmadi;T. Constandinou;C. Bouganis
近年来，局部场电位（LFPs）已成为脑机接口（BMI）的一种有前途的替代输入信号。多项研究表明，基于LFP的BMI可以提供长期记录稳定性，同时具有与尖峰对应物相当的解码性能。然而，尽管有令人信服的结果，大多数基于LFP的BMI仍然使用手工制作的功能，这是一个耗时的过程，可能是次优的。在本文中，我们提出了一种基于时间卷积网络（TCN）的端到端系统方法，以自动提取特征并直接从原始LFP信号中解码手部运动的运动学。我们将其解码性能与传统方法进行基准测试，这些方法包含由手工制作的LFP功能驱动的长短期记忆（LSTM）解码器。实验结果表明，所提出的方法相比传统的方法，显着的性能改善，证明了适用性和潜力的TCN为基础的端到端系统提供稳定和高解码性能的基于LFP的BMI。
In recent years, local field potentials (LFPs) have emerged as a promising alternative input signal for brain-machine interfaces (BMIs). Several studies have demonstrated that LFP-based BMIs could provide long-term recording stability and, at the same time, comparable decoding performance to their spike counterparts. However, despite the compelling results, most LFP-based BMIs still make use of hand-crafted features which is a time-consuming process and can be suboptimal. In this paper, we propose an end-to-end system approach based on temporal convolutional network (TCN) to automatically extract features and decode kinematics of hand movements directly from raw LFP signals. We benchmark its decoding performance against traditional approaches incorporating long short-term memory (LSTM) decoders driven by hand-crafted LFP features. Experimental results demonstrate significant performance improvement of the proposed approach compared to the traditional approaches, demonstrating the suitability and the potential of TCN-based end-to-end systems in providing stable and high decoding performance LFP-based BMIs.