Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled limb continuously. In addition to decoding the target position, accurate decoding of force amplitude is essential for designing BCI systems capable of performing fine movements like grasping. In this study, we proposed a stack Long Short-Term Memory (LSTM) neural network which was able to accurately predict the force amplitude applied by three freely moving rats using their Local Field Potential (LFP) signal. The performance of the network was compared with the Partial Least Square (PLS) method. The average coefficient of correlation (r) for three rats were 0.67 in PLS and 0.73 in LSTM based network and the coefficient of determination ( $$R^{2}$$ ) were 0.45 and 0.54 for PLS and LSTM based network, respectively. The network was able to accurately decode the force values without explicitly using time lags in the input features. Additionally, the proposed method was able to predict zero-force values very accurately due to benefiting from an output nonlinearity. The proposed stack LSTM structure was able to predict applied force from the LFP signal accurately. In addition to higher accuracy, these results were achieved without explicitly using time lags in input features which can lead to more accurate and faster BCI systems.

中文翻译：

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用于从主运动皮层（M1）的局部场电势信号解码连续力的堆栈LSTM结构

脑计算机接口（BCI）将神经系统的活动转换为控制信号，可为外部设备解释。使用连续电动机BCI，用户将能够连续控制机械臂或肢体残疾。除了对目标位置进行解码之外，对力幅值进行准确的解码对于设计能够执行诸如抓握之类的精细运动的BCI系统至关重要。在这项研究中，我们提出了一个堆栈长短期记忆（LSTM）神经网络，该网络能够使用其局部场电位（LFP）信号准确预测三只自由移动大鼠施加的力幅度。将网络性能与偏最小二乘（PLS）方法进行了比较。三只大鼠的平均相关系数（r）在PLS中为0.67，在0中为0。在基于LSTM的网络中为73，确定系数（$$ R ^ {2} $$）对于基于PLS和LSTM的网络分别为0.45和0.54。网络能够准确解码力值，而无需在输入特征中明确使用时间滞后。另外，由于受益于输出非线性，所提出的方法能够非常精确地预测零力值。所提出的堆栈LSTM结构能够根据LFP信号准确预测作用力。除了更高的准确性外，这些结果的获得还没有明确使用输入功能中的时滞，而这会导致更准确，更快的BCI系统。网络能够准确解码力值，而无需在输入特征中明确使用时间滞后。另外，由于受益于输出非线性，所提出的方法能够非常精确地预测零力值。所提出的堆栈LSTM结构能够根据LFP信号准确预测作用力。除了更高的准确性外，这些结果的获得还没有明确使用输入功能中的时滞，而这会导致更准确，更快的BCI系统。网络能够准确解码力值，而无需在输入特征中明确使用时间滞后。另外，由于受益于输出非线性，所提出的方法能够非常精确地预测零力值。所提出的堆栈LSTM结构能够根据LFP信号准确预测作用力。除了更高的准确性外，这些结果的获得还没有明确使用输入功能中的时滞，而这会导致更准确，更快的BCI系统。