Automatic eye-blink artifact removal method based on EMD-CCA

Soomro, Mumtaz Hussain; Badruddin, Nasreen; Yusoff, Mohd Zuki; Jatoi, Munsif Ali

doi:10.1109/iccme.2013.6548236

Cited by 39 publications

(19 citation statements)

References 9 publications

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“…This EEG signal is not pure in form; rather it has different contaminations. These contaminations are subjected to certain artefact removal techniques to make it possible for further processing for various EEG applications [5][6][7].The estimation of the sources responsible for electromagnetic activity inside the brain based on the potential recorded through the electrodes is one of major applications of EEG. This problem is termed as EEG source localization or EEG inverse problem as the model is estimated with the available data set [8].…”

Section: Introductionmentioning

confidence: 99%

EEG based brain source localization comparison of sLORETA and eLORETA

Jatoi

Kamel

Malik

et al. 2014

Australas Phys Eng Sci Med

118

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Human brain generates electromagnetic signals during certain activation inside the brain. The localization of the active sources which are responsible for such activation is termed as brain source localization. This process of source estimation with the help of EEG which is also known as EEG inverse problem is helpful to understand physiological, pathological, mental, functional abnormalities and cognitive behaviour of the brain. This understanding leads for the specification for diagnoses of various brain disorders such as epilepsy and tumour. Different approaches are devised to exactly localize the active sources with minimum localization error, less complexity and more validation which include minimum norm, low resolution brain electromagnetic tomography (LORETA), standardized LORETA, exact LORETA, Multiple Signal classifier, focal under determined system solution etc. This paper discusses and compares the ability of localizing the sources for two low resolution methods i.e., sLORETA and eLORETA respectively. The ERP data with visual stimulus is used for comparison at four different time instants for both methods (sLORETA and eLORETA) and then corresponding activation in terms of scalp map, slice view and cortex map is discussed.

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Section: Introductionmentioning

confidence: 99%

EEG based brain source localization comparison of sLORETA and eLORETA

Jatoi

Kamel

Malik

et al. 2014

Australas Phys Eng Sci Med

118

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“…The effectiveness of EEMD-CCA is shown to be better than that of the ICA and the wavelet methods [44]. However, no study has been carried out on the performance of CCA-EEMD.…”

Section: Discussionmentioning

confidence: 95%

Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising

Mowla

Zilany

et al. 2015

Biomedical Signal Processing and Control

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“…An IMF is an analytical, self-constructed, well-defined, data-driven function whose amplitudes and frequencies vary with time [17]. such that [19]:…”

Section: Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

A comparison of techniques and technologies for SSVEP classification

Tello¹,

Müller²,

Bastos³

et al. 2014

5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC)

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This paper presents the evaluation of seven techniques of feature extraction (PSD, F-Test, EMD, MCE, CCA, LASSO and MSI) for gaze-target detections in a SSVEP-based BCI. Two type of technologies for visual stimulation were used (LCD and LEDs). Five differents windows lengths (1, 2, 4, 5 and 10 s) were used and seven volunteers participated in this study. The highest accuracy obtained in all cases was 93.57% using LEDs and the highest ITR was 36.90 bits/min for LCD.The technique based on MSI shows the highest success rate in both cases (LCD or LED) and is even more noticeable when the window size is increased.

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Automatic eye-blink artifact removal method based on EMD-CCA

Cited by 39 publications

References 9 publications

EEG based brain source localization comparison of sLORETA and eLORETA

EEG based brain source localization comparison of sLORETA and eLORETA

Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising

A comparison of techniques and technologies for SSVEP classification

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