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EEG signal artefact removal using flower pollination fractional calculus optimisation

International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 262-276页

作者： Jayalaxmi Anem G.Sateeshkumar R.Madhu E.C.E Aditya Institute of Technology and ManagementSrikakulamIndia E.C.E JNTUKKakinadaIndia

Purpose-The main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during ***,pre-processing is done on EEG signal for quality ***,by using wavelet transform(WT)feature extraction is *** artefacts present in the EEG are removed using deep *** deep convlstm is trained by proposed fractional calculus based flower pollination optimisation ***/methodology/approach-Nowadays’EEG signals play vital role in the field of neurophysiologic *** activities of human can be analysed by using EEG *** signals are frequently affected by noise during acquisition and other external disturbances,which lead to degrade the signal *** of EEG signals is necessary for the effective usage of signals in any *** paper proposes a new technique named as flower pollination fractional calculus optimisation(FPFCO)algorithm for the removal of artefacts fromEEGsignal through deep learning *** the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep *** existed FPO algorithm is used for solution update through global and local *** this case,the fractional calculus(FC)method attempts to include the past solution by including the second order *** a result,the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization(FPO)***,5 EEGsignals are contaminated by artefacts such asEMG,EOG,EEGand *** contaminatedEEG signals are pre-processed to remove baseline and power line ***,feature extraction is done by using WTand extracted features are applied to deep convlstm,which is trained by proposed fractional calculus based flower pollination optimisation a

关键词： Wavelet transform deep convlstm Flower pollination optimisation algorithm Fractional calculus EEG MSE RMSE SNR

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