title: Classifying ECoG/EEG-based motor imagery tasks.
abstract: The multichannel electrocorticogram (ECoG)/electroencephalogram (EEG) signals are commonly used to classify two kinds of motor imagery (MI) tasks. In this paper, the ECoG and EEG data sets are composed of training and test data, which are recorded during different time/days. Power spectral density (PSD) is selected as features; Fisher discriminant analysis (FDA) and common spatial patterns (CSP) are used to filter redundancy; K-Nearest-Neighbor (KNN) classifier is applied to classify MI tasks; and a new function R (k) is presented to estimate the value of k. Using these methods, we obtain the predictive accuracy of MI tasks based on ECoG data (which is 92%) and EEG data (which is 81%). The results show that we can effectively classify two kinds of MI tasks based on EEG as well as ECoG.
authors.affiliation: Dept. of Electron. Sci. & Technol., USTC, Hefei Anhui, China. ban@mail.ustc.edu.cn
authors.forename: Bin
authors.initials: B
authors.lastname: An
authors.forename: Yan
authors.initials: Y
authors.lastname: Ning
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Jiang
authors.forename: Huanqing
authors.initials: H
authors.lastname: Feng
authors.forename: Heqin
authors.initials: H
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology; D001931:Brain Mapping; D016000:Cluster Analysis; D004566:Electrodes; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods; D005071:Evoked Potentials; D019054:Evoked Potentials, Motor; D006801:Humans; D010363:Pattern Recognition, Automated; D010465:Perception; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D014584:User-Computer Interface