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Spatio-temporal pattern analysis of single-trial EEG signals recorded during visual object recognition

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Abstract

Object discrimination is a fundamental cognitive function for human brains. In this study, we utilized an analysis approach for single-trial electroencephalography (EEG) to analyze the spatio-temporal activation patterns of visual objects processing in human brains, and attempted to apply it to discriminate different visual objects. The spatial patterns were respectively extracted from scalp EEG and the reconstructed cortical sources, while the experiment participants were perceiving 4 different categories of visual objects (faces, buildings, cats and cars). By classifying the patterns extracted from single-trial EEG, the presented visual objects could be discriminated by a computer, and the classification accuracies may provide a quantitative index to evaluate the spatial differences of the brain activations related to different visual objects. Our results demonstrated that the spatial patterns on both the scalp and the sources levels resulted in higher classification accuracies than chance rate. We also examined the classification results using temporally non-overlapping time intervals of different event-related potential (ERP) components. The temporal changes of classification accuracies may reflect the temporal distribution of the discriminative information in the EEG responses. The present pattern extraction and classification methods may compose a computational model for single-trial EEG data analysis in investigations of the spatio-temporal activation patterns for object recognition. These spatio-temporal patterns in EEG may be useful to identifying the discriminative spatial areas and time stages to improve the accuracies and efficiencies of object discrimination. In addition, investigating and utilizing humans’ cognitive mechanisms of object recognition may improve computers’ processing efficiency of visual information.

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Authors and Affiliations

  1. National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China

    ChangMing Wang & Li Yao

  2. College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China

    Li Yao, Shi Xiong & JiaCai Zhang

  3. Biomedical Imaging Technology Center, Emory University/Georgia Institute of Technology, Atlanta, 30322, USA

    XiaoPing Hu

Authors
  1. ChangMing Wang
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  2. XiaoPing Hu
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  3. Li Yao
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  4. Shi Xiong
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  5. JiaCai Zhang
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Corresponding author

Correspondence to JiaCai Zhang.

Additional information

WANG ChangMing was born in 1982. He received the bachelor’s degree in information engineering from Zhejiang University, Hangzhou, China in 2005. He is currently a Ph.D. candidate in National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University. His research interests include EEG/ERP signal processing, EEG-based mind reading and brain-computer interface. He is a member of Beijing Psychological Society.

YAO Li was born in 1961. She received the Ph.D. degree in pattern recognition and intelligent system from Institute of Automation, Chinese Academy of Sciences, Beijing, China in 1998. Currently, she is a professor in National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University. Her research interests include Methodology in cognitive neuroscience, neuro-engineering and mind reading. Dr. Yao is a member of Chinese Society of Neural Network, as well as the deputy director of Beijing Institute of Electronics Professional Committee in Education.

ZHANG JiaCai was born in 1975. He received the Ph.D. degree in pattern recognition and intelligent system from Institute of Automation, Chinese Academy of Sciences, Beijing, China in 2004. Currently, he is an associate professor in Beijing Normal University. His research interests include braincomputer interface, brain signal processing, and neurofeedback.

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Wang, C., Hu, X., Yao, L. et al. Spatio-temporal pattern analysis of single-trial EEG signals recorded during visual object recognition. Sci. China Inf. Sci. 54, 2499–2507 (2011). https://doi.org/10.1007/s11432-011-4507-1

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  • DOI: https://doi.org/10.1007/s11432-011-4507-1

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