93 research outputs found
Decoding arm kinematic parameters from motor cortical ensemble activity using long short-term memory
Department of Human Factors EngineeringBrain machine interface (BMI) is the interface which converts the neural signal recorded from the subject into the intention such as arm movement or grasping. One of the key topic in the BMI research area is to decode the neural signal from the motor cortex in the brain and extract information related with the movement such as velocity, position and speed of arm. By interpreting the signal, the tetraplegia can get a chance to overcome the obstacle by controlling the robot arm according to their intention interacting with the environment.
To achieve these goal, several algorithms such as Kalman Filter or optimal linear estimation have been used. Further, modern machine learning algorithms such as Long Short-Term Memory(LSTM) However, most of the decoders have focused on the decoding of the velocity parameter even though there were evidences that the neural activity of the primary motor cortex encodes the directional information and speed information differently.
The developed decoder in this research reconstructed the velocity of an arm movement with two separate LSTM decoder, which is designed for individual decoding of speed and direction. Because there is neural evidence that the direction is encoded nonlinearly in the motor cortex and the speed variable has nonlinear characteristics, the nonlinear prediction algorithm, which is LSTM, was used as the predictor. Also, velocity Kalman filter (VKF) and velocity LSTM (VLSTM) were compared with the decoder. The performance was measured for the data provided from the Collaborative Research in Computational Neuroscience - Data sharing (CRCNS). The task was center ??? out reaching movement and the primary motor cortex signal of macaque monkey was recorded. Specifically, the correctness of the velocity reconstruction was tested with following measurements: angular difference, correlation coefficient, mean absolute error. Further, the correctness of the position reconstruction was tested with following measurements: Euclidean distance with true trajectory. Also, the effectiveness of the reconstructed trajectory was measured with following indexes: Euclidean distance with straight line from home to target, hit rate, distance to target according to the movement time, movement directional change (MDC), orthogonal directional change (ODC).
The results in aspects of how accurately the decoder predicted the kinematics showed that the new decoder predicts the movement direction accurately than the other two decoders. The quality of position reconstruction of the SDLSTM was significantly better than the velocity Kalman filter and was similar with the VLSM. Also, the SDLSTM???s trajectory could be acquired the target in highest frequency. Also, the model complexity of the speed and direction predictor in the SDLSTM were different with each other. It implies that the encoding strategy of the two variables in the motor cortex can be different. Finally, we could identify that the SDLSTM increased the overall decoding performance.clos
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