title: MEG based classification of wrist movement.
abstract: 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.
authors: School of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
authors: Nasim
authors: N
authors: Montazeri
authors: Mohammad Bagher
authors: MB
authors: Shamsollahi
authors: Sepideh
authors: S
authors: Hajipour
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface; D014955:Wrist Joint / Q000502:physiology*