title: Transductive domain adaptive learning for epileptic electroencephalogram recognition.
abstract: OBJECTIVE
  Intelligent recognition of electroencephalogram (EEG) signals is an important means for epilepsy detection. Almost all conventional intelligent recognition methods assume that the training and testing data of EEG signals have identical distribution. However, this assumption may indeed be invalid for practical applications due to differences in distributions between the training and testing data, making the conventional epilepsy detection algorithms not feasible under such situations. In order to overcome this problem, we proposed a transfer-learning-based intelligent recognition method for epilepsy detection.
  
  
  METHODS
  We used the large-margin-projected transductive support vector machine method (LMPROJ) to learn the useful knowledge between the training domain and testing domain by calculating the maximal mean discrepancy. The method can effectively learn a model for the testing data with training data of different distributions, thereby relaxing the constraint that the data distribution in the training and testing samples should be identical.
  
  
  RESULTS
  The experimental validation is performed over six datasets of electroencephalogram signals with three feature extraction methods. The proposed LMPROJ-based transfer learning method was compared with five conventional classification methods. For the datasets with identical distribution, the performance of these six classification methods was comparable. They all could achieve an accuracy of 90%. However, the LMPROJ method obviously outperformed the five conventional methods for experimental datasets with different distribution between the training and test data. Regardless of the feature extraction method applied, the mean classification accuracy of the proposed method is above 93%, which is greater than that of the other five methods with statistical significance.
  
  
  CONCLUSIONS
  The proposed transfer-learning-based method has better classification accuracy and adaptability than the conventional methods in classifying EEG signals for epilepsy detection.
authors.affiliation: School of Digital Media, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu Province 214122, PR China.
authors.forename: Changjian
authors.initials: C
authors.lastname: Yang
authors.affiliation: School of Digital Media, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu Province 214122, PR China; Department of Biomedical Engineering, University of California, Davis, One Shields Avenue, Davis, CA 95616-5270, USA. Electronic address: dengzhaohong@jiangnan.edu.cn.
authors.forename: Zhaohong
authors.initials: Z
authors.lastname: Deng
authors.affiliation: Centre for Smart Health, School of Nursing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Electronic address: kschoi@ieee.org.
authors.forename: Kup-Sze
authors.initials: KS
authors.lastname: Choi
authors.affiliation: School of Digital Media, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu Province 214122, PR China.
authors.forename: Yizhang
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: School of Digital Media, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu Province 214122, PR China.
authors.forename: Shitong
authors.initials: S
authors.lastname: Wang
mesh_terms: D004569:Electroencephalography; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D007858:Learning; D008962:Models, Theoretical
keywords: Electroencephalogram; Epilepsy detection; Kernel principal component analysis; Short time Fourier transform; Transfer learning; Wavelet packet decomposition