Low-Rank Linear Dynamical Systems for Motor Imagery EEG

Abstract

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from “BCI Competition III Dataset IVa” and “BCI Competition IV Database 2a.” The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP.

Asynchronous brain-computer interface shared control of robotic grasping

“…Therefore, the maximum accuracy of ID classifying the "rest" and MI states by LDA is approximately 80% when the threshold is 0.62. Aiming to design the MIDC, Low-Rank Linear Dynamical Systems (LR-LDS) modeling [22] is applied for EEG signal feature extraction. The training EEG signal matrix X can be decomposed as…”

“…Among these paradigms, MI is the most frequently used one because of the natural spontaneous signals used for building a BCI. MI-based BCI detects the changes of mu (8)(9)(10)(11)(12) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28) rhythms according to the ERD/ERS potentials. However, MI signal analysis is a very challenging task to complete because of the low signal-to-noise ratio.…”

A Review of EEG-Based Brain-Computer Interface Systems Design

“…Spatiospectral dual-feature matrix could be resulted simultaneously without much preprocess or post-process. Then, low-rank linear dynamical systems (LR-LDS) [33] were proposed by decomposing feature subspace of LDSs on finite Grassmannian space and obtained a good performance. With the rapid development of deep learning for big data training, this method is used more and more widely in EEG pattern recognition.…”