Academic Journal
Real-time motor imagery-based brain–computer interface system by implementing a frequency band selection
| العنوان: | Real-time motor imagery-based brain–computer interface system by implementing a frequency band selection |
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| المؤلفون: | Abdul Ameer Abbas, Ali, Martínez García, Herminio |
| المساهمون: | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. EPIC - Energy Processing and Integrated Circuits |
| بيانات النشر: | Springer Nature |
| سنة النشر: | 2023 |
| المجموعة: | Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
| مصطلحات موضوعية: | Àrees temàtiques de la UPC::Enginyeria electrònica, Brain-computer interfaces, Motor imagery-based brain–computer interface (MI-BCI), Event-related desynchronization and synchronization (ERD/ERS), Finite impulse response (FIR), Common spatial patterns (CSP), Short-time Fourier transform (STFT), Real-time systems, Interfícies cervell-ordinador |
| الوصف: | Motor imagery-based brain–computer interfaces (MI-BCIs) are a promise to revolutionize the way humans interact with machinery or software, performing actions by just thinking about them. Patients suffering from critical movement disabilities, such as amyotrophic lateral sclerosis (ALS) or tetraplegia, could use this technology to interact more independently with their surroundings. This paper aims to aid communities affected by these disorders with the development of a method that is capable of detecting the intention to execute movements in the upper extremities of the body. This will be done through signals acquired with an electroencephalogram (EEG), their conditioning and processing, and their subsequent classification with artificial intelligence models. In addition, a digital signal filter will be designed to keep the most characteristic frequency bands of each individual and increase accuracy significantly. After extracting discriminative statistical, frequential, and spatial features, it was possible to obtain an 88% accuracy on validation data with a random forest (RF) model when it came to detecting whether a participant was imagining a left-hand or a right-hand movement. Furthermore, a convolutional neural network (CNN) was used to distinguish if the participant was imagining a movement or not, which achieved 78% accuracy and 90% precision. These results will be verified by implementing a real-time simulation with the usage of a robotic arm. ; Peer Reviewed ; Postprint (author's final draft) |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2191-4281 |
| Relation: | https://link.springer.com/article/10.1007/s13369-023-08024-z; Abdul Ameer Abbas, A.; Martinez, H. Real-time motor imagery-based brain–computer interface system by implementing a frequency band selection. "Arabian Journal for Science and Engineering", 20 Juny 2023, Vol. 48, pp. 15099-15113; http://hdl.handle.net/2117/394218 |
| DOI: | 10.1007/s13369-023-08024-z |
| الاتاحة: | http://hdl.handle.net/2117/394218 https://doi.org/10.1007/s13369-023-08024-z |
| Rights: | Attribution-NonCommercial-NoDerivatives 4.0 International ; http://creativecommons.org/licenses/by-nc-nd/4.0/ ; Open Access |
| رقم الانضمام: | edsbas.42E06975 |
| قاعدة البيانات: | BASE |
| تدمد: | 21914281 |
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| DOI: | 10.1007/s13369-023-08024-z |