scholarly journals EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Valeria Mondini ◽  
Anna Lisa Mangia ◽  
Angelo Cappello
Keyword(s):  
Motor Imagery ◽  
Training Session ◽  
Adaptive Strategy ◽  
Support Vector ◽  
Common Spatial Pattern ◽  
User Training ◽  
Common Control ◽  
Adaptive Thresholds ◽  
Giving Feedback ◽  
Bci System

Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system’s efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system’s “flexibility” and “customizability,” namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback.

scholarly journals Development of a Novel Motor Imagery Control Technique and Application in a Gaming Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Ting Li ◽  
Jinhua Zhang ◽  
Tao Xue ◽  
Baozeng Wang
Keyword(s):  
Motor Imagery ◽  
Control Technique ◽  
System Operation ◽  
Common Spatial Pattern ◽  
Spatial Features ◽  
3D Game ◽  
Gaming Environment ◽  
Game Environment ◽  
And Control ◽  
Bci System

We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player’s BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP) and the proposed multifeature extraction. To demonstrate the effectiveness of 3D game environment at enhancing player’s event-related desynchronization (ERD) and event-related synchronization (ERS) production ability, we set the 2D Screen Game as the comparison experiment. According to a series of statistical results, the group performing MI in the 3D Tetris environment showed more significant improvements in generating MI-associated ERD/ERS. Analysis results of game-score indicated that the players’ scores presented an obvious uptrend in 3D Tetris environment but did not show an obvious downward trend in 2D Screen Game. It suggested that the immersive and rich-control environment for MI would improve the associated mental imagery and enhance MI-based BCI skills.

scholarly journals Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects

2021 ◽  
Vol 15 ◽  
Author(s):  
Qing Zhou ◽  
Jiafan Lin ◽  
Lin Yao ◽  
Yueming Wang ◽  
Yan Han ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Supplementary Motor Area ◽  
Support Vector ◽  
Relative Power ◽  
Common Spatial Pattern ◽  
Neurophysiological Mechanism ◽  
Computer Interfaces ◽  
Performance Variation ◽  
Performance Variations ◽  
And Task

One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.

scholarly journals The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

2021 ◽  
Vol 7 ◽  
pp. e374
Author(s):  
Mamunur Rashid ◽  
Bifta Sama Bari ◽  
Md Jahid Hasan ◽  
Mohd Azraai Mohd Razman ◽  
Rabiu Muazu Musa ◽  
...  
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Motor Imagery ◽  
Communication Strategy ◽  
Support Vector ◽  
Random Subspace ◽  
Common Spatial Pattern ◽  
Feature Selection Technique ◽  
Linear Discriminant ◽  
Feature Dimension

Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier’s performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.

scholarly journals Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Qingshan She ◽  
Haitao Gan ◽  
Yuliang Ma ◽  
Zhizeng Luo ◽  
Tom Potter ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Clustering Algorithm ◽  
Gaussian Mixture ◽  
Dimensional Subspace ◽  
Support Vector ◽  
Imagery Task ◽  
Common Spatial Pattern ◽  
Significant Information ◽  
Low Dimensional ◽  
Composite Data

Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs. While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency bands from all channels in the same scale as the intrinsic mode functions (IMFs), identifying and extracting the specific IMFs that contain significant information remain difficult. In this paper, a novel method has been developed to identify the information-bearing components in a low-dimensional subspace without prior knowledge. Our method trains a Gaussian mixture model (GMM) of the composite data, which is comprised of the IMFs from both the original signal and noise, by employing kernel spectral regression to reduce the dimension of the composite data. The informative IMFs are then discriminated using a GMM clustering algorithm, the common spatial pattern (CSP) approach is exploited to extract the task-related features from the reconstructed signals, and a support vector machine (SVM) is applied to the extracted features to recognize the classes of EEG signals during different motor imagery tasks. The effectiveness of the proposed method has been verified by both computer simulations and motor imagery EEG datasets.

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 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.

Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine

2020 ◽  
Vol 91 (3) ◽  
pp. 034106 ◽  
Author(s):  
Fei Wang ◽  
Zongfeng Xu ◽  
Weiwei Zhang ◽  
Shichao Wu ◽  
Yahui Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Spatial Pattern ◽  
Motor Imagery ◽  
Filter Bank ◽  
Support Vector ◽  
Common Spatial Pattern

scholarly journals A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

2020 ◽  
Vol 10 (11) ◽  
pp. 864
Author(s):  
Omneya Attallah ◽  
Jaidaa Abougharbia ◽  
Mohamed Tamazin ◽  
Abdelmonem A. Nasser
Keyword(s):  
Motor Imagery ◽  
Assistive Devices ◽  
Support Vector ◽  
Svm Classifier ◽  
Motor Disabilities ◽  
Daily Routines ◽  
Daily Life Activities ◽  
Assistive Systems ◽  
Future Work ◽  
Bci System

Motor deficiencies constitute a significant problem affecting millions of people worldwide. Such people suffer from a debility in daily functioning, which may lead to decreased and incoherence in daily routines and deteriorate their quality of life (QoL). Thus, there is an essential need for assistive systems to help those people achieve their daily actions and enhance their overall QoL. This study proposes a novel brain–computer interface (BCI) system for assisting people with limb motor disabilities in performing their daily life activities by using their brain signals to control assistive devices. The extraction of useful features is vital for an efficient BCI system. Therefore, the proposed system consists of a hybrid feature set that feeds into three machine-learning (ML) classifiers to classify motor Imagery (MI) tasks. This hybrid feature selection (FS) system is practical, real-time, and an efficient BCI with low computation cost. We investigate different combinations of channels to select the combination that has the highest impact on performance. The results indicate that the highest achieved accuracies using a support vector machine (SVM) classifier are 93.46% and 86.0% for the BCI competition III–IVa dataset and the autocalibration and recurrent adaptation dataset, respectively. These datasets are used to test the performance of the proposed BCI. Also, we verify the effectiveness of the proposed BCI by comparing its performance with recent studies. We show that the proposed system is accurate and efficient. Future work can apply the proposed system to individuals with limb motor disabilities to assist them and test their capability to improve their QoL. Moreover, the forthcoming work can examine the system’s performance in controlling assistive devices such as wheelchairs or artificial limbs.

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