Online Adaptive Filters to Classify Left and Right Hand Motor Imagery
| العنوان: | Online Adaptive Filters to Classify Left and Right Hand Motor Imagery |
|---|---|
| المؤلفون: | Ridha Djemal, Olivier Romain, Kais Belwafi, Sofien Gannouni, Fakhreddine Ghaffari, Bouraoui Ouni |
| المصدر: | BIOSIGNALS Scopus-Elsevier |
| بيانات النشر: | SCITEPRESS - Science and and Technology Publications, 2016. |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | Left and right, medicine.diagnostic_test, Computer science, business.industry, 0206 medical engineering, Feature extraction, 02 engineering and technology, Electroencephalography, Linear discriminant analysis, 020601 biomedical engineering, Adaptive filter, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, Control system, medicine, Computer vision, Artificial intelligence, business, MATLAB, computer, 030217 neurology & neurosurgery, computer.programming_language |
| الوصف: | Sensorimotor rhythms (SMRs) caused by motor imagery are key issues for subject with severe disabilities when controlling home devices. However, the development of such EEG-based control system requires a great effort to reach a high accuracy in real-time. Furthermore, BCIs have to confront with inter-individual variability, imposing to the parameters of the methods to be adapted to each subjects. In this paper, we propose a novel EEG-based solution to classify right and left hands(RH and LH) thoughts. Our approach integrates adaptive filtering techniques customized for each subject during the training phase to increase the accuracy of the proposed system. The validation of the proposed architecture is conducted using existing data sets provided by BCI-competition and then using our own on-line validation platform experienced with four subjects. Common Spatial Pattern (CSP) is used for feature extraction to extract features vector from µ and I² bands. These features are classified by the Linear Discriminant Analysis (LDA) algorithm. Our prototype integrates the Open-BCI acquisition system with 8 channels connected to Matlab environment in which we integrated all EEG signal processing including the adaptive filtering. The proposed system achieves 80.5% of classification accuracy, which makes approach a promising method to control an external devices based on the thought of LH and RH movement. |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3ba0abca1d03fc0c7f259421c97c4cbTest https://doi.org/10.5220/0005846503350339Test |
| رقم الانضمام: | edsair.doi.dedup.....d3ba0abca1d03fc0c7f259421c97c4cb |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |