scholarly journals CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data

2021 ◽  
Author(s):  
Md Ochiuddin Miah ◽  
Rafsanjani Muhammod ◽  
Khondaker Abdullah Al Mamun ◽  
Dewan Md. Farid ◽  
Shiu Kumar ◽  
...  
Keyword(s):  
Real Time ◽  
Motor Imagery ◽  
Classification Performance ◽  
Input Devices ◽  
Ensemble Technique ◽  
Source Codes ◽  
Eeg Data ◽  
Time Motor ◽  
Bio Engineering

Background: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data. Proposed Method: To enhance the classification performance of real-time BCI applications, this paper presents a new clustering-based ensemble technique called CluSem to mitigate this problem. We also develop a new brain game called CluGame using this method to evaluate the classification performance of real-time motor imagery movements. In this game, real-time EEG signal classification and prediction tabulation through animated balls are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. Results: Our results demonstrate that CluSem is able to improve the classification accuracy between 5% and 15% compared to the existing methods on our collected as well as the publicly available EEG datasets. The source codes used to implement CluSem and CluGame are publicly available at https://github.com/MdOchiuddinMiah/MI-BCI_ML.

scholarly journals Prediction of Motor Imagery Tasks from Multi-Channel EEG Data for Brain-Computer Interface Applications

2020 ◽  
Author(s):  
Md. Ochiuddin Miah ◽  
Md. Mahfuzur Rahman ◽  
Rafsanjani Muhammod ◽  
Dewan Md. Farid
Keyword(s):  
Real Time ◽  
Motor Imagery ◽  
Support Vector ◽  
Eeg Signal ◽  
Ensemble Technique ◽  
Brain Signals ◽  
Eeg Data ◽  
Bio Engineering ◽  
The Brain

AbstractThe classification of motor imagery electroencephalogram (MI-EEG) is a pivotal part of the biosignal classification in the brain-computer interface (BCI) applications. Currently, this bio-engineering based technology is being employed by researchers in various fields to develop cutting edge applications. The classification of real-time MI-EEG signal is the core computing and challenging task in these applications. It is well-known that the existing classification methods are not so accurate due to the high dimensionality and dynamic behaviors of the real-time EEG data. To improve the classification performance of real-time BCI applications, this paper presents a clustering-based ensemble technique and a developed brain game that distinguishes different human thoughts. At first, we have gathered the brain signals, extracted and selected informative features from these signals to generate training and testing sets. After that, we have constructed several classifiers using Artificial Neural Network (ANN), Support Vector Machine (SVM), naïve Bayes, Decision Tree (DT), Random Forest, Bagging, AdaBoost and compared the performance of these existing approaches with suggested clustering-based ensemble technique. On average, the proposed ensemble technique improved the classification accuracy of roughly 5 to 15% compared to the existing methods. Finally, we have developed the targeted brain game employing our suggested ensemble technique. In this game, real-time EEG signal classification and prediction tabulation through animated ball are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. All relevant codes are available via open repository at: https://github.com/mrzResearchArena/MI-EEG.

scholarly journals Fuzzy Integral With Particle Swarm Optimization for CNN-Based Motor-Imagery EEG Classification

2021 ◽  
Author(s):  
Jian-Xue Huang ◽  
Chia-Ying Hsieh ◽  
Ya-Lin Huang ◽  
Chun-Shu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Motor Imagery ◽  
Particle Swarm ◽  
Classification Performance ◽  
Fuzzy Integral ◽  
Ensemble Classifiers ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Eeg Data ◽  
Model Training

Recently, decoding human electroencephalographic (EEG) data using convolutional neural network (CNN) has driven the state-of-the-art recognition of motor-imagery EEG patterns for brain-computer interfacing (BCI). While a variety of CNN models have been used to classify motor-imagery EEG data, it is unclear if aggregating an ensemble of heterogeneous CNN models could further enhance the classification performance. To integrate the outputs of ensemble classifiers, this work utilizes fuzzy integral with particle swarm optimization (PSO) to estimate optimal confidence levels assigned to classifiers. The proposed framework aggregates CNN classifiers and fuzzy integral with PSO, achieving robust performance in single-trial classification of motor-imagery EEG data across various CNN model training schemes depending on the scenarios of BCI usage. This proof-of-concept study demonstrates the feasibility of applying fuzzy fusion techniques to enhance CNN-based EEG decoding and benefit practical applications of BCI.

Feature subset and time segment selection for the classification of EEG data based motor imagery

2020 ◽  
Vol 61 ◽  
pp. 102026
Author(s):  
Jie Wang ◽  
Zuren Feng ◽  
Xiaodong Ren ◽  
Na Lu ◽  
Jing Luo ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Feature Subset ◽  
Time Segment ◽  
Eeg Data ◽  
Selection For

scholarly journals A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface

2020 ◽  
Vol 14 ◽  
Author(s):  
Yan Chen ◽  
Wenlong Hang ◽  
Shuang Liang ◽  
Xuejun Liu ◽  
Guanglin Li ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Structural Information ◽  
Classification Performance ◽  
Target Domain ◽  
Alternating Direction ◽  
Learning Procedure ◽  
Eeg Data ◽  
Support Matrix ◽  
Model Training ◽  
Learning Machine

In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data for model training, which prolongs the calibration session. From the perspective of transfer learning, the model knowledge inherent in reference subjects incorporating few target EEG data have the potential to solve the above issue. Thus, a novel knowledge-leverage-based support matrix machine (KL-SMM) was developed to improve the classification performance when only a few labeled EEG data in the target domain (target subject) were available. The proposed KL-SMM possesses the powerful capability of a matrix learning machine, which allows it to directly learn the structural information from matrix-form EEG data. In addition, the KL-SMM can not only fully leverage few labeled EEG data from the target domain during the learning procedure but can also leverage the existing model knowledge from the source domain (source subject). Therefore, the KL-SMM can enhance the generalization performance of the target classifier while guaranteeing privacy protection to a certain extent. Finally, the objective function of the KL-SMM can be easily optimized using the alternating direction method of multipliers method. Extensive experiments were conducted to evaluate the effectiveness of the KL-SMM on publicly available MI-based EEG datasets. Experimental results demonstrated that the KL-SMM outperformed the comparable methods when the EEG data were insufficient.

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