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Sequence-to-sequence deep neural network with spatio-spectro and temporal features for motor imagery classification

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Electroencephalography (EEG) is a method of the brain–computer interface (BCI) that measures brain activities. EEG is a method of (non-)invasive recording ofthe electrical activity ofthe brain. This can be used to build BCIs. From the last decade, EEG has grasped researchers' attention to distinguish human activities. However, temporal information has rarely been retained to incorporate temporal information for multi-class (more than two classes) motor imagery classification. This research proposes a long-short-term-memory-based deep learning model to learn the hidden sequential patterns. Two types of features are used to feed the proposed model, including Fourier Transform Energy Maps (FTEMs) and Common Spatial Patterns (CSPs) filters. Multiple experiments have been conducted on a publicly available dataset. Extraction of spatial and spectro-temporal features using CSP filters and FTEM allow the sequence-tosequence based proposed model to learn the hidden sequential features. The proposed method is trained, evaluated, and optimized for a publicly available benchmark data set and resulted in 0.81 mean kappa value. Obtained results depict the model robustness for the artifacts and suitable for real-life applications with comparable classification accuracy. The code and findings will be available at https://github.com/waseemabbaas/Motor-Imagery-Classification.git.
Twórcy
  • School of Computer Engineering and Sciences, Shanghai University, Shanghai, China
autor
  • Cloud Application Solutions Division, Mentor, A Siemens Business, Lahore, Pakistan
  • Neurosurgery Department, Nishtar Medical University, Multan, Pakistan
autor
  • Department of Computer & Software Engineering, National University of Sciences & Technology, Islamabad, Pakistan
  • Department of Computer Sciences, Government College University Faisalabad, Pakistan
  • Department of Information & Technology, Bahauddin Zakariya University, Multan, Pakistan
  • Institute of Research & Advanced Studies, Multan, Pakistan
autor
  • School of Computer Engineering and Sciences, Shanghai University, Shanghai, China; Shanghai Institute of Applied Mathematics and Mechanics, Shanghai, China
Bibliografia
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a238fedb-34c8-46c0-a810-877efea9f1d1
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