Convolutional Networks Outperform Linear Decoders in Predicting EMG From Spinal Cord Signals.
Convolutional Networks Outperform Linear Decoders in Predicting EMG From Spinal Cord Signals.
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DOI：
10.3389/fnins.2018.00689
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发表时间：
2018
影响因子：
4.3
通讯作者：
Sahin M
中科院分区：
文献类型：
--
作者：
Guo Y;Gok S;Sahin M
需要先进的算法来揭示神经和行为数据之间的复杂关系。在这项研究中，前肢肌电图 (EMG) 信号是根据大鼠皮质脊髓束 (CST) 的多个电极阵列 (MEA) 记录的多单元神经信号重建的。将六层卷积神经网络 (CNN) 与预测 EMG 信号的线性解码器进行比较。该网络包含三个依赖于会话的整流线性单元 (ReLU) 特征层和三个在会话之间共享的 Gamma 函数层。尽管前肢位置在大部分行为持续时间内不受限制，但在多只动物的单个会话中进行重建时，确定系数 (R2) 值超过 0.2，相关性超过 0.5。 CNN 的性能明显优于线性解码器，并且模型响应比大鼠神经肌肉系统的激活持续时间更长。这些发现表明，CNN 模型隐式地根据神经信号预测了熟练的前肢运动的短期动态。这些结果令人鼓舞，神经信号处理中的类似问题可以使用简单分析函数定义的 CNN 变体来解决。可以开发低功耗固件来在实时应用中容纳这些 CNN 解决方案。
Advanced algorithms are required to reveal the complex relations between neural and behavioral data. In this study, forelimb electromyography (EMG) signals were reconstructed from multi-unit neural signals recorded with multiple electrode arrays (MEAs) from the corticospinal tract (CST) in rats. A six-layer convolutional neural network (CNN) was compared with linear decoders for predicting the EMG signal. The network contained three session-dependent Rectified Linear Unit (ReLU) feature layers and three Gamma function layers were shared between sessions. Coefficient of determination (R2) values over 0.2 and correlations over 0.5 were achieved for reconstruction within individual sessions in multiple animals, even though the forelimb position was unconstrained for most of the behavior duration. The CNN performed visibily better than the linear decoders and model responses outlasted the activation duration of the rat neuromuscular system. These findings suggest that the CNN model implicitly predicted short-term dynamics of skilled forelimb movements from neural signals. These results are encouraging that similar problems in neural signal processing may be solved using variants of CNNs defined with simple analytical functions. Low powered firmware can be developed to house these CNN solutions in real-time applications.
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影响因子：
4
作者：
Lawhern, Vernon J.;Solon, Amelia J.;Lance, Brent J.
通讯作者：
Lance, Brent J.
DOI：
10.1115/1.3140702
发表时间：
1985-01-01
影响因子：
1.7
作者：
HOGAN, N
通讯作者：
HOGAN, N
影响因子：
1.9
作者：
Hogan, Neville;Sternad, Dagmar
通讯作者：
Sternad, Dagmar
影响因子：
4.3
作者：
Guo, Yi;Foulds, Richard A.;Sahin, Mesut
通讯作者：
Sahin, Mesut
影响因子：
3.5
作者：
Hajinoroozi, Mehdi;Mao, Zijing;Huang, Yufei
通讯作者：
Huang, Yufei