Hybrid ICA-Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals

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2016 nî lūn-bûn

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2016年の論文

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2016年論文

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2016年論文

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2016年論文

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2016年論文

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2016年論文

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2016年论文

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2016年论文

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2016年论文

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name

Hybrid ICA-Regression: Automat ...... lectroencephalographic Signals

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type

Item

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Hybrid ICA-Regression: Automat ...... lectroencephalographic Signals

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Hybrid ICA-Regression: Automat ...... lectroencephalographic Signals

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P1476

Hybrid ICA-Regression: Automat ...... lectroencephalographic Signals

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P2093

Malik M Naeem Mannan

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Muhammad A Kamran

http://www.w3.org/2001/XMLSchema#string

Myung Y Jeong

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P2860

The correction of eye blink artefacts in the EEG: a comparison of two prominent methods

Multiscale entropy analysis of biological signals

PhysioBank, PhysioToolkit, and PhysioNet : Components of a New Research Resource for Complex Physiologic Signals

Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters

Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

Combining EEG and eye tracking: identification, characterization, and correction of eye movement artifacts in electroencephalographic data.

Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, Kurtosis, and wavelet-ICA.

Data-driven analysis of simultaneous EEG/fMRI using an ICA approach.

Automatic removal of eye movement and blink artifacts from EEG data using blind component separation.

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193

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scholarly article

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10.3389/FNHUM.2016.00193

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2016-05-03T00:00:00Z

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301826744

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27199714

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P921

electroencephalography

automation

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4853904

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