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      Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

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          Abstract

          At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.

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          Most cited references26

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          Ensemble sparse classification of Alzheimer's disease.

          The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer's disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classifier (SRC) method, which has shown to be effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface.

            Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually.
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              Probabilistic Common Spatial Patterns for Multichannel EEG Analysis.

              Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a probabilistic modeling setting. Specifically, probabilistic CSP (P-CSP) is proposed as a generic EEG spatio-temporal modeling framework that subsumes the CSP and regularized CSP algorithms. The proposed framework enables us to resolve the overfitting issue of CSP in a principled manner. We derive statistical inference algorithms that can alleviate the issue of local optima. In particular, an efficient algorithm based on eigendecomposition is developed for maximum a posteriori (MAP) estimation in the case of isotropic noise. For more general cases, a variational algorithm is developed for group-wise sparse Bayesian learning for the P-CSP model and for automatically determining the model size. The two proposed algorithms are validated on a simulated data set. Their practical efficacy is also demonstrated by successful applications to single-trial classifications of three motor imagery EEG data sets and by the spatio-temporal pattern analysis of one EEG data set recorded in a Stroop color naming task.
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                Author and article information

                Contributors
                Journal
                Front Aging Neurosci
                Front Aging Neurosci
                Front. Aging Neurosci.
                Frontiers in Aging Neuroscience
                Frontiers Media S.A.
                1663-4365
                08 July 2016
                2016
                : 8
                : 172
                Affiliations
                [1] 1School of Information Science and Engineering, Yanshan University Qinhuangdao, China
                [2] 2The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University Qinhuangdao, China
                [3] 3School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology Qinhuangdao, China
                [4] 4School of Basic Medicine, Xinxiang Medical University Xinxiang, China
                Author notes

                Edited by: Junfeng Sun, Shanghai Jiao Tong University, China

                Reviewed by: Ramesh Kandimalla, Emory University, USA; Wanzeng Kong, Hangzhou Dianzi University, China

                *Correspondence: Yanhong Zhou zhouyanhong_02@ 123456126.com Chengbiao Lu johnlu9000@ 123456hotmail.com
                Article
                10.3389/fnagi.2016.00172
                4937019
                27458376
                00c2e28a-28ef-4995-9163-05a69d7f146a
                Copyright © 2016 Wen, Jia, Lian, Zhou and Lu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor 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.

                History
                : 20 January 2016
                : 28 June 2016
                Page count
                Figures: 1, Tables: 1, Equations: 0, References: 39, Pages: 9, Words: 4834
                Categories
                Neuroscience
                Mini Review

                Neurosciences
                sparse representation-based classification,sparse representation,eeg signal,preclinical mild cognitive impairment,mild cognitive impairment,alzheimer’s disease,epilepsy,brain computer interface

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