Dimensionality Reduction of Local Field Potential Features with Convolution Neural Network in Neural Decoding: A Pilot Study.

Abstract

Local field potentials (LFPs) have better long-term stability compared with spikes in brain-machine interfaces (BMIs). Many studies have shown promising results of LFP decoding, but the high-dimensional feature of LFP still hurdle the development of the BMIs to low-cost. In this paper, we proposed a framework of a 1D convolution neural network (CNN) to reduce the dimensionality of the LFP features. For evaluating the performance of this architecture, the reduced LFP features were decoded to cursor position (Center-out task) by a Kalman filter. The Principal components analysis (PCA) was also performed as a comparison. The results showed that the CNN model could reduce the dimensionality of LFP features to a smaller size without significant performance loss. The decoding result based on the CNN features outperformed that based on the PCA features. Moreover, the reduced features by CNN also showed robustness across different sessions. These results demonstrated that the LFP features reduced by the CNN model achieved low cost without sacrificing high-performance and robustness, suggesting that this method could be used for portable BMI systems in the future.