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      A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

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          Abstract

          Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA) model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI) competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods.

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          Most cited references 44

          • Record: found
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          • Article: not found

          Least squares quantization in PCM

           S E Lloyd (1982)
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            • Record: found
            • Abstract: not found
            • Article: not found

            Updating quasi-Newton matrices with limited storage

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Floating search methods in feature selection

                Bookmark

                Author and article information

                Affiliations
                1Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences , Beijing, China
                2College of Life Science and Technology, Huazhong Agricultural University , Wuhan, China
                3Neural Interface & Rehabilitation Technology Research Center, Huazhong University of Science and Technology , Wuhan, China
                Author notes

                Edited by: Mikhail Lebedev, Duke University, United States

                Reviewed by: Zhong Yin, University of Shanghai for Science and Technology, China; Yufeng Ke, Tianjin University, China

                *Correspondence: Jin Zhou sisun819@ 123456yahoo.com

                This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                16 April 2018
                2018
                : 12
                5911500 10.3389/fnins.2018.00217
                Copyright © 2018 Han, Zhao, Sun, Chen, Ke, Xu, Zhang, Zhou and Wang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Counts
                Figures: 8, Tables: 12, Equations: 21, References: 44, Pages: 20, Words: 10438
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 31320103914
                Award ID: 31370987
                Award ID: 81622027
                Categories
                Neuroscience
                Original Research

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