Analyzing the performance of segmented trajectory reconstruction of lower limb movements from EEG signals with combinations of electrodes, gaps, and delays. (July 2021)

• Record Type: Journal Article
• Authors: Mercado, Luis; Quiroz-Compean, Griselda; Azorín, José M.
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Brain–machine interface -- Electroencephalography -- Continuous trajectory reconstruction -- Lower limb kinematics; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102783
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Trajectory reconstruction of specific segments gives better results than a single reconstruction for the entire trajectory. Regions C, P, and F of the 10–20 system are most relevant for lower limb trajectory reconstruction. The optimal delay applied to the decoder covers 3 s with 9 equally sized gaps. Abstract: Objective: Brain–machine interfaces have performed continuous trajectory reconstruction of limb movements from brain signals relying on multiple linear regression. Most reported approaches deal with the reconstruction of the entire motion trajectory using a single regression and choosing its parameters arbitrarily. This study proposes the reconstruction of trajectories dividing them on phases and proposing a regression for each phase. The parameters for each regressor were selected according to their influence in the performance of the trajectory reconstruction. Methods: Isotonic flexions and extensions of the hip and knee were segmented in phases and a linear regressor was proposed for each phase. The number of electrodes, gaps, and delays of these regressors were selected using an exhaustive comprehensive search to improve the correlation coefficient, normalized root mean square error, and signal-to-noise ratio of the reconstructed trajectory. Results: The most frequent electrodes in the trajectory reconstructions between subjects with good performance were electrodes Fz, C3, C4, Cz, P3, P4, and Pz. The combination of a delay of 3 s with 9 gaps gave better performances in general. Conclusions: In this study it was appreciated that the electrodes that mainly contribute to the trajectory reconstruction are located around the mid-scalp. Also, it was appreciated that the information of movement in the electrical activity is located around 3 s before the movement. Significance: The set of parameters obtained could be helpful to define a limited numbers of electrodes. The delay and number of data samples could also be helpful to establish better experimental setups.