Applying a Locally Linear Embedding Algorithm for Feature Extraction and Visualization of MI-EEG

Abstract

Robotic-assisted rehabilitation system based on Brain-Computer Interface (BCI) is an applicable solution for stroke survivors with a poorly functioning hemiparetic arm. The key technique for rehabilitation system is the feature extraction of Motor Imagery Electroencephalography (MI-EEG), which is a nonlinear time-varying and nonstationary signal with remarkable time-frequency characteristic. Though a few people have made efforts to explore the nonlinear nature from the perspective of manifold learning, they hardly take into full account both time-frequency feature and nonlinear nature. In this paper, a novel feature extraction method is proposed based on the Locally Linear Embedding (LLE) algorithm and DWT. The multiscale multiresolution analysis is implemented for MI-EEG by DWT. LLE is applied to the approximation components to extract the nonlinear features, and the statistics of the detail components are calculated to obtain the time-frequency features. Then, the two features are combined serially. A backpropagation neural network is optimized by genetic algorithm and employed as a classifier to evaluate the effectiveness of the proposed method. The experiment results of 10-fold cross validation on a public BCI Competition dataset show that the nonlinear features visually display obvious clustering distribution and the fused features improve the classification accuracy and stability. This paper successfully achieves application of manifold learning in BCI.

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

“…Approaches used for MI EEG analysis include the short-time Fourier transform (STFT) [55], the wavelet transform (WT) [75] and the discrete wavelet transform (DWT) [76]. Decomposition methods such as the WT and the DWT are powerful since different EEG signal frequency bands contain different information about MI actions [11,23], and they can be used to decompose a signal in multiresolution and multiscale [77,78,79]. The DWT and WT are competent in deriving dynamic features, which is particularly important in EEG signals since they are non-stationary, non-linear and non-Gaussian [11].…”

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

“…An EEG-based motor planning exercise was investigated by Mammone et al (2020), where a time-frequency map, generated through beamforming and CWT, was utilized as input to the CNN. Decomposition techniques, for instance, DWT and WPD, are efficacious because significant information is carried in different EEG bands (Kevric and Subasi, 2017), and these approaches are capable of decomposing the brain waves at multiresolution and multiscale (Li et al, 2016a). Moreover, they are able to extract dynamic features, which is crucial for EEG signals due to their non-stationary and non-linear characteristics (Kevric and Subasi, 2017).…”

Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation

“…Specifically, imagining motor actions usually modifies the amplitude of the mu/beta rhythms in the sensory-motor cortex [22]. These variations in the spectral content of EEG signals can be used to control a BCI system [23,24].…”