Title :
Classifying ECoG/EEG-Based Motor Imagery Tasks
Author :
An, Bin ; Ning, Yan ; Jiang, Zhaohui ; Feng, Huanqing ; Zhou, Heqin
Author_Institution :
Dept. of Electron. Sci. & Technol., USTC, Hefei
fDate :
Aug. 30 2006-Sept. 3 2006
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
Keywords :
electroencephalography; filtering theory; neurophysiology; pattern classification; signal classification; ECoG-EEG-based motor imagery task; Fisher discriminant analysis; common spatial patterns; electroencephalogram; filter redundancy; k-nearest-neighbor classifier; multichannel electrocorticogram; power spectral density; signal classification; training; Cities and towns; Data mining; Electrodes; Electroencephalography; Feature extraction; Filtering; Filters; Frequency; Spatial resolution; Testing; ECoG; EEG; KNN; MI;
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2006.259567
