Brain Computer Interface Using Motor Imagery and Facial Expressions to Control a Mobile Robot

2018 ◽  
Author(s):  
James Kuffuor ◽  
Biswanath Samanta
Keyword(s):  
Facial Expression ◽  
Mobile Robot ◽  
Facial Expressions ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Hybrid Approach ◽  
Computer Interface ◽  
Support Vector ◽  
Correct Identification ◽  
Characteristic Features

A study is presented on brain computer interface (BCI) using motor imagery (MI) and facial expressions to control a mobile robot. Traditionally, only MI signals are used in BCI applications. In this paper a hybrid approach of using both MI and facial expression stimulations for BCI is proposed. Electroencephalography (EEG) signals were acquired using a sensor system and processed for several MI and facial expressions to extract characteristic features. The features were used to train support vector machine (SVM) based classifiers and the trained classifiers were used to recognize test signals for correct identification of MI and facial expressions. A system was developed to implement the BCI using MI and facial expressions to control a mobile robot. Results of training using MI and facial expressions, individually and together are presented for comparison. The combined features from MI and facial expression stimulations were found to give performance similar to facial expressions but better than MI only.

EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface

2011 ◽  
Vol 3 (3) ◽  
pp. 43-56 ◽  
Author(s):  
Ling Zou ◽  
Xinguang Wang ◽  
Guodong Shi ◽  
Zhenghua Ma
Keyword(s):  
Support Vector Machine ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Average Power ◽  
Computer Interface ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Reconstructed Signal

Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the sixth level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.

scholarly journals Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Chu Kiong Loo ◽  
Andrews Samraj ◽  
Gin Chong Lee
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Estimation Methods ◽  
Support Vector ◽  
Linear Discriminant

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method.

scholarly journals Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yilu Xu ◽  
Jing Hua ◽  
Hua Zhang ◽  
Ronghua Hu ◽  
Xin Huang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Motor Imagery ◽  
Optimization Problem ◽  
Brain Computer Interface ◽  
The Other ◽  
Computer Interface ◽  
Geometric Feature ◽  
Support Vector ◽  
Transductive Support Vector Machine ◽  
Calibration Time

Long and tedious calibration time hinders the development of motor imagery- (MI-) based brain-computer interface (BCI). To tackle this problem, we use a limited labelled set and a relatively large unlabelled set from the same subject for training based on the transductive support vector machine (TSVM) framework. We first introduce an improved TSVM (ITSVM) method, in which a comprehensive feature of each sample consists of its common spatial patterns (CSP) feature and its geometric feature. Moreover, we use the concave-convex procedure (CCCP) to solve the optimization problem of TSVM under a new balancing constraint that can address the unknown distribution of the unlabelled set by considering various possible distributions. In addition, we propose an improved self-training TSVM (IST-TSVM) method that can iteratively perform CSP feature extraction and ITSVM classification using an expanded labelled set. Extensive experimental results on dataset IV-a from BCI competition III and dataset II-a from BCI competition IV show that our algorithms outperform the other competing algorithms, where the sizes and distributions of the labelled sets are variable. In particular, IST-TSVM provides average accuracies of 63.25% and 69.43% with the abovementioned two datasets, respectively, where only four positive labelled samples and sixteen negative labelled samples are used. Therefore, our algorithms can provide an alternative way to reduce the calibration time.

EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface

2013 ◽  
pp. 57-69
Author(s):  
Ling Zou ◽  
Xinguang Wang ◽  
Guodong Shi ◽  
Zhenghua Ma
Keyword(s):  
Support Vector Machine ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Average Power ◽  
Computer Interface ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Classification Rate ◽  
Transform Method ◽  
Linear Discriminant

Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the sixth level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.

Introduction to Motor Imagery-Based Brain-Computer Interface

2020 ◽  
pp. 168-197
Author(s):  
Nitesh Singh Malan ◽  
Shiru Sharma
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Extraction Methods ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Complex Wavelet Transform ◽  
Feature Extraction Method ◽  
Time Frequency

In this chapter, motor imagery (MI) based brain-computer interface (BCI) is introduced incorporating the explanation of key components required to design a practical BCI device. Its application to the medical and nonmedical sector is discussed in detail. In the experimental study, a feature extraction method using time, frequency, and phase analysis of Motor imagery EEG is presented. For the classification of MI task, EEG signals are decomposed using a dual-tree complex wavelet transform (DTCWT) and then time, frequency, and phase features are extracted. The validation of the proposed method is conducted using BCI competition IV dataset 2b. A Support vector machine (SVM) classifier is used to perform the classification task. Performance of the proposed method is compared with the standard feature extraction methods. The proposed scheme achieved a larger average classification accuracy of 82.81% which is better than that obtained by other methods.

scholarly journals Mobile robot control based on noninvasive brain-computer interface using hierarchical classifier of imagined motor commands

2018 ◽  
Vol 161 ◽  
pp. 03003 ◽  
Author(s):  
Filipp Gundelakh ◽  
Lev Stankevich ◽  
Konstantin Sonkin
Keyword(s):  
Mobile Robot ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Non Invasive ◽  
Vector Machines ◽  
Artefact Detection ◽  
Supervisor Control ◽  
Mobile Robot Control ◽  
Hierarchical Classifier

The study describes approaches of direct and supervisor control of a mobile robot based on a non-invasive brain-computer interface. An interface performs electroencephalographic signal decoding, which includes several steps: filtering, artefact detection, feature extraction, and classification. In this study, a classifier with hierarchical structure was developed and applied. Description of a committee of classifiers based on neural networks and support vector machines is given. The developed classifier demonstrated accuracy 50 ± 5% of single trial decoding of four classes of imaginary fine movements. Prospects of using non-invasive brain-computer interface for control of mobile robots was described. Key applications of the system are maintenance of immobilized patients and rehabilitation procedures both in clinic and at home.

scholarly journals Discriminating three motor imagery states of the same joint for brain-computer interface

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12027
Author(s):  
Shan Guan ◽  
Jixian Li ◽  
Fuwang Wang ◽  
Zhen Yuan ◽  
Xiaogang Kang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Cloud Model ◽  
Brain Computer Interface ◽  
Laboratory Data ◽  
Computer Interface ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Shoulder Abduction ◽  
Common Spatial Pattern

The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.

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