A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm

Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals...

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Veröffentlicht in:PloS one Jg. 18; H. 9; S. e0276133
Hauptverfasser: Khan, Rabia Avais, Rashid, Nasir, Shahzaib, Muhammad, Malik, Umar Farooq, Arif, Arshia, Iqbal, Javaid, Saleem, Mubasher, Khan, Umar Shahbaz, Tiwana, Mohsin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: San Francisco Public Library of Science 08.09.2023
Public Library of Science (PLoS)
Schlagworte:
Accuracy
Algorithms
Analysis
Artificial intelligence
Biology and Life Sciences
Brain
Brain research
Classification
Communications systems
Competition
Computer and Information Sciences
Computer applications
Datasets
Decision analysis
Decision trees
Disabilities
Discriminant analysis
EEG
Electroencephalography
Engineering and Technology
Evaluation
Feature extraction
Feature recognition
Human-computer interface
Image classification
Implants
Independent component analysis
Literature reviews
Logistic regression
Medicine and Health Sciences
Mental task performance
Physical Sciences
Preprocessing
Regression
Regression analysis
Research and Analysis Methods
Robotics
Signal classification
Support vector machines
Pakistan
ISSN:1932-6203, 1932-6203
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Beschreibung
Zusammenfassung:Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application of independent component analysis (ICA) on raw EEG data, accompanied by the employment of common spatial pattern (CSP) and log-variance for extracting useful features. Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. The proposed framework achieved the best classification accuracies with logistic regression classifier for both datasets. Average classification accuracy of 90.42% has been attained on BCI Competition IV dataset 1 for seven different subjects, while for BCI Competition III dataset 4a, an average accuracy of 95.42% has been attained on five subjects. This indicates that the model can be used in real time BCI systems and provide extra-ordinary results for 2-class Motor Imagery (MI) signals classification applications and with some modifications this framework can also be made compatible for multi-class classification in the future.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0276133