Yan Ning
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yan Ning has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Bin An
Department of Electronic Science & Technology, USTC, POBox4 Hefei Anhui, 230027 China
2 shared publications - Heqin Zhou
Department of Automation, University of Science and Technology of China, Hefei, Anhui 230027, China. phone: 086-551-3601510; fax: 086-551-3601522
2 shared publications - Huanqing Feng
Department of Electronic Science & Technology, University of Science and Technology of China, Hefei, Anhui 230027, China
2 shared publications - Zhaohui Jiang
Department of Electronic Science & Technology, USTC, POBox4 Hefei Anhui, 230027 China
2 shared publications
Latest Publications
- 1 August 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2006 (1557170X) , 6339-6342
Classifying ECoG/EEG-Based Motor Imagery Tasks
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
- 1 August 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2006 (1557170X) , 6339-6342
Classifying ECoG/EEG-Based Motor Imagery Tasks
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
- 1 August 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 1557170X,p. 6339-6342
Classifying ECoG/EEG-Based Motor Imagery Tasks
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
- 1 August 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 1557170X,p. 6339-6342
Classifying ECoG/EEG-Based Motor Imagery Tasks
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