Feature extraction of four-class motor imagery EEG signals based on functional brain network

Abstract

Objective. A motor-imagery-based brain-computer interface (MI-BCI) provides an alternative way for people to interface with the outside world. However, the classification accuracy of MI signals remains challenging, especially with an increased number of classes and the presence of high variations with data from multiple individual people. This work investigates electroencephalogram (EEG) signal processing techniques, aiming to enhance the classification performance of multiple MI tasks in terms of tackling the challenges caused by the vast variety of subjects. Approach. This work introduces a novel method to extract discriminative features by combining the features of functional brain networks with two other feature extraction algorithms: common spatial pattern (CSP) and local characteristic-scale decomposition (LCD). After functional brain networks are established from the MI EEG signals of the subjects, the measures of degree in the binary networks are extracted as additional features and fused with features in the frequency and spatial domains extracted by the CSP and LCD algorithms. A real-time BCI robot control system is designed and implemented with the proposed method. Subjects can control the movement of the robot through four classes of MI tasks. Both the BCI competition IV dataset 2a and real-time data acquired in our designed system are used to validate the performance of the proposed method. Main results. As for the offline data experiment results, the average classification accuracy of the proposed method reaches 79.7%, outperforming the majority of popular algorithms. Experimental results with real-time data also prove the proposed method to be highly promising in its real-time performance. Significance. The experimental results show that our proposed method is robust in extracting discriminative brain activity features when performing different MI tasks, hence improving the classification accuracy in four-class MI tasks. The high classification accuracy and low computational demand show a considerable practicality for real-time rehabilitation systems.

A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding

“…The region of the tongue MI task network seems to cover a wider region and has a weaker connection than other tasks, because the tongue task would call more brain area compared with other tasks. Ai et al [ 30 ] find tongue task calls more neurons, which strongly supports our findings. According to the electrode position in Figure 6 , the connection of right-hand MI task is concentrated in the 7th-12th channels significantly, which corresponds to the area of the brain activity.…”

“…Yang and Gao [ 29 ] proposed a multivariate weighted ordinal pattern transition (MWOPT) network to analyse the driving fatigue behaviour and obtain high accuracy. Ai et al [ 30 ] constructed a single-layer brain network by canonical correlation analysis and combined CSP and local characteristic-scale decomposition to extract the feature of MI signals. Besides, the multifrequency network, as the single-layer network development, can analyse the system from different perspectives.…”

A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery

“…Most of the studies reported in the literature focused on sensor-based BCIs. First, raw sensor data are filtered into two groups in this study: 0.1-30 Hz and µ rhythm (8)(9)(10)(11)(12)(13). Table 2 lists the classification success of sensor data for 118 channels.…”

“…Alazrai et al [8] reported success in finger movements, Lu et al [9] controlled a vehicle using EEG signals, and Xygonakis et al [10], in 2018, studied four-class motor imagery in the EEG source space and improved its accuracy compared to the sensor data analysis. Qingsong et al [11] analyzed four-task motor imagery in 2019, while Zhang et al [12] reported in 2019 that children can successfully use BCIs.…”

Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

“…Ai et al proposed a feature extraction method that combined the features of brain function network and local characteristic-scale decomposition (LCD) together. The good performance of this method was verified on the self-designed real-time BCI robot control and has put forward four classes of dataset [42]. In addition, to measure the complexity of EEG time series, Wang et al proposed a fusion entropy (sample entropy, approximate entropy, and spectral entropy) analysis method for EEG and EOG signals.…”