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.

scholarly journals An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Kui Feng ◽  
Jing Jin ◽  
Ian Daly ◽  
Jiale Zhou ◽  
Yugang Niu ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
Channel Selection ◽  
Selection Method ◽  
Selection Methods ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Novel Method ◽  
Electroencephalogram Eeg

Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy

2021 ◽  
Vol 12 (3) ◽  
pp. 96-115
Author(s):  
Manal Tantawi ◽  
Aya Naser ◽  
Howida Shedeed ◽  
Mohammed Fahmy Tolba
Keyword(s):  
Support Vector Machines ◽  
Epileptic Seizures ◽  
Bat Algorithm ◽  
Line Length ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Vector Machines ◽  
Electroencephalogram Eeg ◽  
Source Of Information

Electroencephalogram (EEG) signals are a valuable source of information for detecting epileptic seizures. However, monitoring EEG for long periods of time is very exhausting and time consuming. Thus, detecting epilepsy in EEG signals automatically is highly appreciated. In this study, three classes, namely normal, interictal (out of seizure time), and ictal (during seizure), are considered. Moreover, a comparative study is provided for the efficient features in literature resulting in a suggested combination of only three discriminative features, namely R'enyi entropy, line length, and energy. These features are calculated from each of the EEG sub-bands. Finally, support vector machines (SVM) classifier optimized using BAT algorithm (BAT-SVM) is introduced by this study for discriminating between the three classes. Experiments were conducted using Andrzejak database. The accomplished experiments and comparisons in this study emphasize the superiority of the proposed BAT-SVM along with the suggested feature set in achieving the best results.

scholarly journals Balanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals

2020 ◽  
Vol 10 (6) ◽  
pp. 6462-6468
Author(s):  
M. Ben Ayed
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Disease Status ◽  
Raspberry Pi ◽  
Support Vector ◽  
Eeg Signals ◽  
Real Time Processing ◽  
Time Processing ◽  
Electroencephalogram Eeg ◽  
Aided Diagnosis

The revolution in technology affects many fields and among them the Healthcare system. The application-based computer was developed to help specialists to detect diseases, and to perform some basics operations. In this paper, focus is given on the proposed attempts to detect Epilepsy Disease (ED). Several Computer-Aided Diagnosis (CAD) methods were used to provide the brain’s disease status according to signals related to brain activities. These applications achieved acceptable results but still have their limitations. An intelligence CAD based on the Balanced Communication-Avoiding Support Vector Machine (BCA-SVM) is proposed to detect ED using Electroencephalogram (EEG) signals. This attempt is implemented on a Raspberry Pi 4 as a real board to ensure real-time processing. The CAD-based on BCA-SVM achieved an accuracy of 99.8% and the execution time was around 3.2s satisfying the real-time requirement.

scholarly journals Multiple graph fusion based on Riemannian geometry for motor imagery classification

2022 ◽  
Author(s):  
Xiaofeng Xie ◽  
Xiaokun Zou ◽  
Tianyou Yu ◽  
Rongnian Tang ◽  
Yao Hou ◽  
...  
Keyword(s):  
Riemannian Manifold ◽  
Motor Imagery ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Eeg Signals ◽  
Spatial Covariance ◽  
Bilinear Mapping ◽  
Subject Specific

AbstractIn motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance the classification performance of motor imagery BCIs. However, the problem of subject-specific bandpass frequency selection frequently arises in Riemannian manifold-based methods. In this study, we propose a multiple Riemannian graph fusion (MRGF) model to optimize the subject-specific frequency band for a Riemannian manifold. After constructing multiple Riemannian graphs corresponding to multiple bandpass frequency bands, graph embedding based on bilinear mapping and graph fusion based on mutual information were applied to simultaneously extract the spatial and spectral features of the EEG signals from Riemannian graphs. Furthermore, with a support vector machine (SVM) classifier performed on learned features, we obtained an efficient algorithm, which achieves higher classification performance on various datasets, such as BCI competition IIa and in-house BCI datasets. The proposed methods can also be used in other classification problems with sample data in the form of covariance matrices.

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

2019 ◽  
Vol 9 (12) ◽  
pp. 372
Author(s):  
Mustafa Yazici ◽  
Mustafa Ulutas ◽  
Mukadder Okuyan
Keyword(s):  
Motor Imagery ◽  
Classification Performance ◽  
Support Vector ◽  
Problem Solution ◽  
Mu Rhythm ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Cortical Sources ◽  
Cortical Level ◽  
Source Signals

Brain–computer interface (BCI) is a technology used to convert brain signals to control external devices. Researchers have designed and built many interfaces and applications in the last couple of decades. BCI is used for prevention, detection, diagnosis, rehabilitation, and restoration in healthcare. EEG signals are analyzed in this paper to help paralyzed people in rehabilitation. The electroencephalogram (EEG) signals recorded from five healthy subjects are used in this study. The sensor level EEG signals are converted to source signals using the inverse problem solution. Then, the cortical sources are calculated using sLORETA methods at nine regions marked by a neurophysiologist. The features are extracted from cortical sources by using the common spatial pattern (CSP) method and classified by a support vector machine (SVM). Both the sensor and the computed cortical signals corresponding to motor imagery of the hand and foot are used to train the SVM algorithm. Then, the signals outside the training set are used to test the classification performance of the classifier. The 0.1–30 Hz and mu rhythm band-pass filtered activity is also analyzed for the EEG signals. The classification performance and recognition of the imagery improved up to 100% under some conditions for the cortical level. The cortical source signals at the regions contributing to motor commands are investigated and used to improve the classification of motor imagery.

scholarly journals A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 145
Author(s):  
Hongquan Qu ◽  
Zhanli Fan ◽  
Shuqin Cao ◽  
Liping Pang ◽  
Hao Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Power Spectral ◽  
Eeg Data ◽  
Vector Machines ◽  
Full Band ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.

CLASSIFICATION OF FOCAL AND NONFOCAL EEG SIGNALS USING FEATURES DERIVED FROM FOURIER-BASED RHYTHMS

2017 ◽  
Vol 17 (07) ◽  
pp. 1740002 ◽  
Author(s):  
PUSHPENDRA SINGH ◽  
RAM BILAS PACHORI
Keyword(s):  
Filter Bank ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Classification ◽  
Eeg Signals ◽  
Input Feature ◽  
The Mean ◽  
A New Technique ◽  
Electroencephalogram Eeg

We propose a new technique for the automated classification of focal and nonfocal electroencephalogram (EEG) signals using Fourier-based rhythms in this paper. The EEG rhythms, namely, delta, theta, alpha, beta and gamma, are obtained using the discrete Fourier transform (DFT)-based filter bank applied on EEG signals. The mean-frequency (MF) and root-mean-square (RMS) bandwidth features are derived using DFT-based computation on rhythms of EEG signals and their envelopes. These derived features, namely, MF and RMS bandwidths have been provided as an input feature set for the classification of focal and nonfocal EEG signals using a least-squares support vector machine (LS-SVM) classifier. We present experimental results obtained from the publicly available database in order to demonstrate the effectiveness of the proposed feature sets for the automated classification of the focal and nonfocal classes of EEG signals. The obtained classification accuracy in this dataset for the automated classification of focal and nonfocal 50 pairs and 750 pairs of EEG signals are 89.7% and 89.52%, respectively.

scholarly journals Multiclass Motor Imagery Recognition of Single Joint in Upper Limb Based on NSGA- II OVO TWSVM

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Shan Guan ◽  
Kai Zhao ◽  
Fuwang Wang
Keyword(s):  
Support Vector Machine ◽  
Motor Imagery ◽  
Back Propagation ◽  
Classification Performance ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Common Spatial Pattern ◽  
Nsga Ii ◽  
Learning Machine ◽  
Electroencephalogram Eeg

In the study of the brain computer interface (BCI) system, electroencephalogram (EEG) signals induced by different movements of the same joint are hard to distinguish. This paper proposes a novel scheme that combined amplitude-frequency (AF) information of intrinsic mode function (IMF) with common spatial pattern (CSP), namely, AF-CSP to extract motor imagery (MI) features, and to improve classification performance, the second generation nondominated sorting evolutionary algorithm (NSGA-II) is used to tune hyperparameters for linear and nonlinear kernel one versus one twin support vector machine (OVO TWSVM). This model is compared with least squares support vector machine (LS-SVM), back propagation (BP), extreme learning machine (ELM), particle swarm optimization support vector machine (PSO-SVM), and grid search OVO TWSVM (GS OVO TWSVM) on our dataset; the recognition accuracy increased by 5.92%, 22.44%, 22.65%, 8.69%, and 5.75%. The proposed method has helped to achieve higher accuracy in BCI systems.

EEG Classification for BCI Based on CSP and SVM-GA

2013 ◽  
Vol 459 ◽  
pp. 228-231 ◽  
Author(s):  
Hao Yang ◽  
Song Wu
Keyword(s):  
Classification Performance ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Data Set ◽  
Linear Discriminant ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain

Electroencephalogram (EEG) is generally used in Brain-Computer Interface (BCI) applications to measure the brain signals. However, the multichannel EEG signals characterized by unrelated and redundant features will deteriorate the classification accuracy. This paper presents a method based on common spatial pattern (CSP) for feature extraction and support vector machine with genetic algorithm (SVM-GA) as a classifier, the GA is used to optimize the kernel parameters setting. The proposed algorithm is performed on data set Iva of BCI Competition III. Results show that the proposed method outperforms the conventional linear discriminant analysis (LDA) in average classification performance.

scholarly journals Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features

2022 ◽  
Vol 19 (2) ◽  
pp. 629
Author(s):  
Yuting Wang ◽  
Shujian Wang ◽  
Ming Xu
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Classification Accuracy ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Landscape Perception ◽  
Landscape Types ◽  
Electroencephalogram Eeg ◽  
Eeg Features

This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical landscape types (forest, wetland, grassland, desert, water, farmland, and city) were selected. Different electroencephalogram (EEG) signals were generated through different inner experiences and feelings felt by people watching video stimuli of the different landscape types. The electroencephalogram (EEG) features were extracted to obtain the mean amplitude spectrum (MAS), power spectrum density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU) in the five frequency bands of delta, theta, alpha, beta, and gamma. According to electroencephalogram (EEG) features, four classifiers including the back propagation (BP) neural network, k-nearest neighbor classification (KNN), random forest (RF), and support vector machine (SVM) were used to classify the landscape types. The results showed that the support vector machine (SVM) classifier and the random forest (RF) classifier had the highest accuracy of landscape recognition, which reached 98.24% and 96.72%, respectively. Among the six classification features selected, the classification accuracy of MAS, PSD, and DE with frequency domain features were higher than those of the spatial domain features of DASM, RASM and DCAU. In different wave bands, the average classification accuracy of all subjects was 98.24% in the gamma band, 94.62% in the beta band, and 97.29% in the total band. This study identifies and classifies landscape perception based on multi-channel EEG signals, which provides a new idea and method for the quantification of human perception.

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