Ocular Artifacts realization through optimized scheme View Full Text


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Article Info

DATE

2022-04-13

AUTHORS

Santosh Kumar Sahoo, Sumant Kumar Mohapatra

ABSTRACT

Brain Computer Interface (BCI) recommended for online real-time processing of EEG signals. Hence, the recording system’s accuracy improved by nullifying of developed artifacts. The goal of the proposed work is to develop an optimized model for detecting and minimizing ocular artifacts. In the proposed work, 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 feature collection, 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 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. More... »

PAGES

1-15

References to SciGraph publications

  • 2019-01-17. Feature Fusion and Classification of EEG/EOG Signals in SOFT COMPUTING AND SIGNAL PROCESSING
  • 2019-12-05. Electric fish optimization: a new heuristic algorithm inspired by electrolocation in NEURAL COMPUTING AND APPLICATIONS
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    http://scigraph.springernature.com/pub.10.1007/s12652-022-03783-3

    DOI

    http://dx.doi.org/10.1007/s12652-022-03783-3

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