LSTM Improves Accuracy of Reaching Trajectory Prediction From Magnetoencephalography Signals

Abstract

Brain-computer interface (BCI) is a promising and very helpful technology. BCI studies have attempted to predict arm movements to control robotic arm depending on movement intentions. However, the low accuracy of movement prediction is a critical challenge in predicting arm movements. The aim of this study was to predict arm movement with high accuracy from non-invasive neural signals using the deep learning algorithm. We compared the prediction accuracies of the conventional method and long short-term memory (LSTM) using non-invasive MEG signals. This is the first study that applied LSTM to predict arm movements from non-invasive neural signals. The coefficients of correlation between real signals and signals predicted by the MLR on the x-, y-, and z-axes were 0.677 ± 0.139 (mean ± SD), 0.689 ± 0.140, and 0.785 ± 0.103, respectively. The coefficients of correlation between real signals and signals predicted by the LSTM on the x-, y-, and z-axes were 0.978 ± 0.004, 0.980 ± 0.005, and 0.980 ± 0.008, respectively. The prediction accuracy was highly improved using the LSTM algorithm. Our results suggest that highly accurate prediction of arm movement is possible without surgery using the deep learning algorithm. We expect that the deep learning algorithm will facilitate the control of a robot arm using non-invasive signals in real life.

Keywords

• Brain-computer interface
• deep learning
• long short-term memory