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Single-Trial Event-Related Potentials Classification via a Discriminative Dictionary Learning Scheme

  • Yue Huang
  • Jun Zhang
  • Xin Chen
  • Delu Zeng
  • Xinghao Ding
  • Dandan Zhang
  • Qingfeng Cai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)

Abstract

Due to the better performances with large number of training samples, algorithms based on sparse representation have received more and more attentions in single-trial event-related potentials classification. Considered the burden from repeating psychological experiments, the classification with less training samples is still a challenge in both cognitive science and pattern recognition. In this paper, a discriminative dictionary learning based scheme is utilized to single-trial ERPs classification, in order to enhance the performance when the training sample size is small. After preprocessing, wavelet is employed to remove the strong background noise at first, and then a sparse representation recognition method based on discriminative dictionary learning, called D-KSVD, is applied to perform the classification on each testing trial. Experiments on ERPs epochs from risk decision test have demonstrated that proposed approach outperforms than existing sparse representation classifier when the training samples decrease dramatically.

Keywords

single-trial ERPs classification sparse representation less training samples dictionary learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yue Huang
    • 1
    • 2
  • Jun Zhang
    • 1
    • 2
  • Xin Chen
    • 1
    • 2
  • Delu Zeng
    • 1
    • 2
  • Xinghao Ding
    • 1
    • 2
  • Dandan Zhang
    • 3
  • Qingfeng Cai
    • 4
  1. 1.Key Lab of Underwater Acoustic Communication and Marine Information TechnologyMinistry of EducationChina
  2. 2.Department of Communication EngineeringXiamen UniversityChina
  3. 3.State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityChina
  4. 4.The school of EconomicsXiamen UniversityChina

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