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A MISO-ARX-Based Method for Single-Trial Evoked Potential Extraction.

In this paper, we propose a novel method for solving the single-trial evoked potential (EP) estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished...

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Published in:BioMed Research International Vol. 2017; pp. 1 - 11
Main Authors: Yu, Nannan, Wu, Lingling, Zou, Dexuan, Chen, Ying, Lu, Hanbing
Format: equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 2/8/2017
Online Access:View this record in EBSCOhost
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        23146133
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      jtl: BioMed Research International
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      dt: 2/8/2017
      vid: 2017
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        121185197
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        10.1155/2017/7395385
        121185197
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        atl: A MISO-ARX-Based Method for Single-Trial Evoked Potential Extraction.
      aug:
        au:
          Yu, Nannan
          Wu, Lingling
          Zou, Dexuan
          Chen, Ying
          Lu, Hanbing
        affil: School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China
      sug:
        subj:
          Evoked Potentials
          Brain Physiology
          Algorithms
          Electroencephalography
          Computer Simulation
          Data Analysis Software
          Experimental Studies
          Funding Source
      ab: In this paper, we propose a novel method for solving the single-trial evoked potential (EP) estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished according to their respective latencies and amplitudes and are extracted simultaneously by multiple-input single-output autoregressive modeling with exogenous input (MISO-ARX). The extraction process is performed in three stages: first, we use a reference EP as a template and decompose it into a set of components, which serve as subtemplates for the remaining steps. Then, a dictionary is constructed with these subtemplates, and EPs are preliminarily extracted by sparse coding in order to roughly estimate the latency of each component. Finally, the single-trial measurement is parametrically modeled by MISO-ARX while characterizing spontaneous electroencephalographic activity as an autoregression model driven by white noise and with each component of the EP modeled by autoregressive-moving-average filtering of the subtemplates. Once optimized, all components of the EP can be extracted. Compared with ARX, our method has greater tracking capabilities of specific components of the EP complex as each component is modeled individually in MISO-ARX. We provide exhaustive experimental results to show the effectiveness and feasibility of our method.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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