scholarly journals Classification of lower limb motor imagery based on iterative EEG source localization and feature fusion

2022 ◽  
Author(s):  
Xiaobo Peng ◽  
Junhong Liu ◽  
Ying Huang ◽  
Yanhao Mao ◽  
Dong Li
Keyword(s):  
Motor Imagery ◽  
Source Localization ◽  
Lower Limb ◽  
Feature Fusion ◽  
Channel Selection ◽  
Single Domain ◽  
Support Vector ◽  
Eeg Classification ◽  
Critical Issues ◽  
Eeg Source Localization

AbstractMotor imagery (MI) brain–computer interface (BCI) systems have broad application prospects in rehabilitation and other fields. However, to achieve accurate and practical MI-BCI applications, there are still several critical issues, such as channel selection, electroencephalogram (EEG) feature extraction and EEG classification, needed to be better resolved. In this paper, these issues are studied for lower limb MI which is more difficult and less studied than upper limb MI. First, a novel iterative EEG source localization method is proposed for channel selection. Channels FC1, FC2, C1, C2 and Cz, instead of the commonly used traditional channel set (TCS) C3, C4 and Cz, are selected as the optimal channel set (OCS). Then, a multi-domain feature (MDF) extraction algorithm is presented to fuse single-domain features into multi-domain features. Finally, a particle swarm optimization based support vector machine (SVM) method is utilized to classify the EEG data collected by the lower limb MI experiment designed by us. The results show that the classification accuracy is 88.43%, 3.35–5.41% higher than those of using traditional SVM to classify single-domain features on the TCS, which proves that the combination of OCS and MDF can not only reduce the amount of data processing, but also retain more feature information to improve the accuracy of EEG classification.

Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition

2020 ◽  
Vol 17 (2) ◽  
pp. 026029 ◽  
Author(s):  
Dharmendra Gurve ◽  
Denis Delisle-Rodriguez ◽  
Maria Romero-Laiseca ◽  
Vivianne Cardoso ◽  
Flavia Loterio ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Matrix Factorization ◽  
Lower Limb ◽  
Channel Selection ◽  
Subject Specific ◽  
Non Negative Matrix Factorization

scholarly journals Densely Feature Fusion Based on Convolutional Neural Networks for Motor Imagery EEG Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 132720-132730 ◽  
Author(s):  
Donglin Li ◽  
Jianhui Wang ◽  
Jiacan Xu ◽  
Xiaoke Fang
Keyword(s):  
Neural Networks ◽  
Motor Imagery ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Eeg Classification

Motor imagery EEG classification based on ensemble support vector learning

2020 ◽  
Vol 193 ◽  
pp. 105464 ◽  
Author(s):  
Jing Luo ◽  
Xing Gao ◽  
Xiaobei Zhu ◽  
Bin Wang ◽  
Na Lu ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Support Vector ◽  
Eeg Classification

scholarly journals Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7309
Author(s):  
Junhyuk Choi ◽  
Keun Tae Kim ◽  
Ji Hyeok Jeong ◽  
Laehyun Kim ◽  
Song Joo Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Motor Imagery ◽  
Lower Limb ◽  
Neurological Disorders ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Lower Limb Exoskeleton ◽  
Electroencephalogram Eeg

This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.

IMPLEMENTATION OF EEG SIGNAL PROCESSING AND DECODING FOR TWO-CLASS MOTOR IMAGERY DATA

2019 ◽  
Vol 31 (04) ◽  
pp. 1950028
Author(s):  
Mohammed Z. Al-Faiz ◽  
Ammar A. Al-hamadani
Keyword(s):  
Motor Imagery ◽  
Channel Selection ◽  
Classification Performance ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Eeg Signal ◽  
Selection Algorithm ◽  
Spatial Filters ◽  
Technological Advantage ◽  
Rbf Kernel

This work decodes two-class motor imagery (MI) based on four main processing steps: (i) Raw electroencephalographic (EEG) signal is decomposed to single trials and spatial filters are estimated for each trial by common spatial filtering (CSP) method; (ii) features are extracted by taking the log transformation (normal distribution) of the spatially filtered EEG signal; (iii) optimal channel selection algorithm is proposed to reduce the number of EEG channels, such approach is regarded as key technological advantage in the implementation of brain–computer interface (BCI) to reduce the system processing time; (iv) finally, support vector machine (SVM) is employed to discriminate two classes of left and right hand MI. Two variations of SVM were proposed: polynomial function kernel and radial-based function RBF kernel. The results revealed that CSP succeeded in removing the strong correlation bound between the EEG samples by maximizing the variance of class 2 samples while minimizing the variance of class 1 samples. The channel selection algorithm achieved its goal to reduce the data dimension by selecting two channels out of three having the lowest variance entropies of 0.239 and 0.261 for channel 1 and channel 2, respectively. The features vector was divided into 80% train and 20% test with five-fold cross validation. The classification performance of SVM-polynomial kernel was 87.86% while it is 95.72% for SVM-RBF kernel as average accuracy of five-folds for both. Thus SVM-RBF is superior to SVM-Poly in the proposed framework.

Assessment of CSP-based two-stage channel selection approach and local transformation-based feature extraction for classification of motor imagery/movement EEG data

2019 ◽  
Vol 64 (6) ◽  
pp. 643-653
Author(s):  
Funda Kutlu Onay ◽  
Cemal Köse
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Channel Selection ◽  
Support Vector ◽  
Local Transformation ◽  
Two Stage ◽  
Linear Discriminant ◽  
One Dimensional ◽  
Brain Functions

Abstract The main idea of brain-computer interfaces (BCIs) is to facilitate the lives of patients having difficulties to move their muscles due to a disorder of their motor nervous systems but healthy cognitive functions. BCIs are usually electroencephalography (EEG)-based, and the success of the BCIs relies on the precision of signal preprocessing, detection of distinctive features, usage of suitable classifiers and selection of effective channels. In this study, a two-stage channel selection and local transformation-based feature extraction are proposed for the classification of motor imagery/movement tasks. In the first stage of the channel selection, the channels were combined according to the neurophysiological information about brain functions acquired from the literature, then averaged and a single channel was formed. In the second stage, selective channels were specified with the common spatial pattern-linear discriminant analysis (CSP-LDA)-based sequential channel removal. After the channel selection phase, the feature extraction was carried out with local transformation-based methods (LTBM): local centroid pattern (LCP), one-dimensional-local gradient pattern (1D-LGP), local neighborhood descriptive pattern (LNDP) and one-dimensional-local ternary pattern (1D-LTP). The distinctions and deficiencies of these methods were compared with other methods in the literature and the classification performances of the k-nearest neighbor (k-NN) and the support vector machines (SVM) were evaluated. As a result, the proposed methods yielded the highest average classification accuracies as 99.34%, 95.95%, 98.66% and 99.90% with the LCP, 1D-LGP, LNDP and 1D-LTP when using k-NN, respectively. The two-stage channel selection and 1D-LTP method showed promising results for recognition of motor tasks. The LTBM will contribute to the development of EEG-based BCIs with the advantages of high classification accuracy, easy implementation and low computational complexity.

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