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Recognition of Ocular Artifacts in EEG Signal through a Hybrid Optimized Scheme.

Brain computer interface (BCI) requires an online and real-time processing of EEG signals. Hence, the accuracy of the recording system is improved by nullifying the developed artifacts. The goal of this proposal is to develop a hybrid model for recognizing and minimizing ocular artifacts through an...

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Published in:BioMed Research International pp. 1 - 26
Main Authors: Sahoo, Santosh Kumar, Mohapatra, Sumant Kumar
Format: algorithm equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 1/17/2022
Online Access:View this record in EBSCOhost
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      dt: 1/17/2022
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2022/4875399
        154721680
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        atl: Recognition of Ocular Artifacts in EEG Signal through a Hybrid Optimized Scheme.
      aug:
        au:
          Sahoo, Santosh Kumar
          Mohapatra, Sumant Kumar
        affil: Department of Electronics & Instrumentation Engineering, CVR College of Engineering, Hyderabad, Telangana, India
      sug:
        subj:
          Artifacts Evaluation
          Signal Processing, Computer Assisted Methods
          Deep Learning
          Electroencephalography
          Human
          Factor Analysis
          Neural Networks (Computer) Methods
          Noise
      ab: Brain computer interface (BCI) requires an online and real-time processing of EEG signals. Hence, the accuracy of the recording system is improved by nullifying the developed artifacts. The goal of this proposal is to develop a hybrid model for recognizing and minimizing ocular artifacts through an improved deep learning scheme. The discrete wavelet transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the features are extracted by principal component analysis (PCA) and independent component analysis (ICA) techniques. After collecting the features, an optimized deformable convolutional network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying the empirical mean curve decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular artifact reduction by the proposed method.
      pubtype: Academic Journal
      doctype:
        algorithm
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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