Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models
Abstract Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within BCI systems by leveraging advanced machine learning and deep learning t...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-07427-2 |
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| author | Abir Das Saurabh Singh Jaejeung Kim Tariq Ahamed Ahanger Anil Audumbar Pise |
| author_facet | Abir Das Saurabh Singh Jaejeung Kim Tariq Ahamed Ahanger Anil Audumbar Pise |
| author_sort | Abir Das |
| collection | DOAJ |
| description | Abstract Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within BCI systems by leveraging advanced machine learning and deep learning techniques. The accurate classification of electroencephalogram (EEG) data is crucial for enhancing BCI performance. The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. The research evaluates the performance of five traditional machine learning classifiers- K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)-using the “PhysioNet EEG Motor Movement/Imagery Dataset”. This dataset encompasses EEG data from various motor tasks, including both actual and imagined movements. Among the traditional classifiers, Random Forest achieved the highest accuracy of 91%, underscoring its efficacy in motor imagery classification within BCI systems. In addition to conventional approaches, the study also explores deep learning techniques, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks yielding accuracies of 88.18% and 16.13%, respectively. However, the proposed hybrid model, which synergistically combines CNN and LSTM, significantly surpasses both traditional machine learning and individual deep learning methods, achieving an exceptional accuracy of 96.06%. This substantial improvement highlights the potential of hybrid deep learning models to advance the state of the art in BCI systems, offering a more robust and precise approach to motor imagery classification. |
| format | Article |
| id | doaj-art-dfde7e732a6c429f99418ff3e1dae1e4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-dfde7e732a6c429f99418ff3e1dae1e42025-08-20T03:42:41ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-07427-2Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning modelsAbir Das0Saurabh Singh1Jaejeung Kim2Tariq Ahamed Ahanger3Anil Audumbar Pise4JCFS, Endicott College, Woosong UniversityAI and Big Data, Endicott College Woosong UniversityDepartment of Computer Science and Engineering, Chungnam National UniversityDepartment of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz UniversityDepartment of Computer Science, Cumulus SolutionsAbstract Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within BCI systems by leveraging advanced machine learning and deep learning techniques. The accurate classification of electroencephalogram (EEG) data is crucial for enhancing BCI performance. The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. The research evaluates the performance of five traditional machine learning classifiers- K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)-using the “PhysioNet EEG Motor Movement/Imagery Dataset”. This dataset encompasses EEG data from various motor tasks, including both actual and imagined movements. Among the traditional classifiers, Random Forest achieved the highest accuracy of 91%, underscoring its efficacy in motor imagery classification within BCI systems. In addition to conventional approaches, the study also explores deep learning techniques, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks yielding accuracies of 88.18% and 16.13%, respectively. However, the proposed hybrid model, which synergistically combines CNN and LSTM, significantly surpasses both traditional machine learning and individual deep learning methods, achieving an exceptional accuracy of 96.06%. This substantial improvement highlights the potential of hybrid deep learning models to advance the state of the art in BCI systems, offering a more robust and precise approach to motor imagery classification.https://doi.org/10.1038/s41598-025-07427-2BCIClassificationEEGMachine learningDeep learningMotor imagery |
| spellingShingle | Abir Das Saurabh Singh Jaejeung Kim Tariq Ahamed Ahanger Anil Audumbar Pise Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models Scientific Reports BCI Classification EEG Machine learning Deep learning Motor imagery |
| title | Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models |
| title_full | Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models |
| title_fullStr | Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models |
| title_full_unstemmed | Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models |
| title_short | Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models |
| title_sort | enhanced eeg signal classification in brain computer interfaces using hybrid deep learning models |
| topic | BCI Classification EEG Machine learning Deep learning Motor imagery |
| url | https://doi.org/10.1038/s41598-025-07427-2 |
| work_keys_str_mv | AT abirdas enhancedeegsignalclassificationinbraincomputerinterfacesusinghybriddeeplearningmodels AT saurabhsingh enhancedeegsignalclassificationinbraincomputerinterfacesusinghybriddeeplearningmodels AT jaejeungkim enhancedeegsignalclassificationinbraincomputerinterfacesusinghybriddeeplearningmodels AT tariqahamedahanger enhancedeegsignalclassificationinbraincomputerinterfacesusinghybriddeeplearningmodels AT anilaudumbarpise enhancedeegsignalclassificationinbraincomputerinterfacesusinghybriddeeplearningmodels |