MEG based classification of wrist movement

Montazeri, N ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/IEMBS.2009.5334472
  3. Abstract:
  4. Neural activity is very important source for data mining and can be used as a control signal for brain-computer interfaces (BCIs). Particularly, Magnetic signals of neurons are enriched with information about the movement of different part of the body such as wrist movement. In this paper, we use MEG (Magneto encephalography) signals of two subjects recorded during wrist movement task in four directions. Data were prepared for BCI competition 2008 for multiclass classification. Our approach for this classification problem consists of PCA as a noise reduction method, ULDA for feature reduction and various linear classifiers such as Bayesian, KNN and SVM. Final results (58%-62% for subject 1 and 36%-40% for subject 2) prove that the suggested method shows better performance compared with other methods
  5. Keywords:
  6. Algorithm ; Article ; Automated pattern recognition ; Computer interface ; Electroencephalography ; Evaluation study ; Evoked response ; Human ; Magnetoencephalography ; Methodology ; Motor cortex ; Movement (physiology) ; Physiology ; Reproducibility ; Sensitivity and specificity ; Wrist ; Evoked potentials ; Pattern recognition, automated ; Reproducibility of results ; User-computer interface ; Wrist joint
  7. Source: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; 2009 , Pages 986-989 ; 1557170X (ISSN) ; 978-142443296-7 (ISBN)
  8. URL: https://www.ncbi.nlm.nih.gov/pubmed/19964746