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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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            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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