Automatic Artifact Removal in EEG Using Independent Component Analysis and One-Class Classification Strategy

Huang, Han-Pang; Liu, Yi-Hung; Wang, Chen‐Chi; Huang, Tz-Hau

doi:10.1166/jnsne.2013.1043

Cited by 6 publications

(3 citation statements)

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“…Firstly, a 4–30 Hz filter was performed on the collected EEG data, and then the artifact of the processed signal was removed to eliminate the interference of artifacts, such as eye electricity, electromyography, and power frequency interference. We used the independent component analysis (ICA) [ 40 , 41 ] module built in the EEGLAB software to reduce the ocular and myoelectric artifacts in EEG. The experimental group and the control group were divided.…”

Section: Methodsmentioning

confidence: 99%

A Methodology for Enhancing SSVEP Features Using Adaptive Filtering Based on the Spatial Distribution of EEG Signals

Wang

Shao

et al. 2023

Micromachines

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In this paper, we propose a classification algorithm of EEG signal based on canonical correlation analysis (CCA) and integrated with adaptive filtering. It can enhance the detection of steady-state visual evoked potentials (SSVEPs) in a brain–computer interface (BCI) speller. An adaptive filter is employed in front of the CCA algorithm to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. The ensemble method is developed to integrate recursive least squares (RLS) adaptive filter corresponding to multiple stimulation frequencies. The method is tested by the SSVEP signal recorded from six targets by actual experiment and the EEG in a public SSVEP dataset of 40 targets from Tsinghua University. The accuracy rates of the CCA method and the CCA-based integrated RLS filter algorithm (RLS-CCA method) are compared. Experiment results show that the proposed RLS-CCA-based method significantly improves the classification accuracy compared with the pure CCA method. Especially when the number of EEG leads is low (three occipital electrodes and five non occipital electrodes), its advantage is more significant, and accuracy reaches 91.23%, which is more suitable for wearable environments where high-density EEG is not easy to collect.

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Section: Methodsmentioning

confidence: 99%

A Methodology for Enhancing SSVEP Features Using Adaptive Filtering Based on the Spatial Distribution of EEG Signals

Wang

Shao

et al. 2023

Micromachines

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“…For , provides a vector of n observed signals x at time n, the Lomb periodogram at frequency f is defined by; (13) Where , are the mean and variance, is a frequency dependent time delay defined to make the periodogram insensitive to time shift. The Lomb-Scargle Periodogram (LSP) was provided to assess the spectral performance and power evaluation of our method to study its effect on the characteristics and signal quality of the original signal after removing artifacts.…”

Section: Fig 4: Refinement Of Very Bad Signals By Iterating the Methodsmentioning

confidence: 99%

A Hybrid Approach for Artifacts Removal from EEG Recordings

Helal¹,

Seddik²,

Hussein³

2017

IJCA

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The electroencephalogram (EEG) is a widely used traditional procedure for diagnosing, monitoring and managing neurological disorders. Many artifact types that often contaminate EEG remain a key challenge for precise diagnosis of brain dysfunctions and neurological disorders. Hence, artifact removal is intuitively required for accurate EEG analysis and treatment. This paper presents a new extensive method that can remove a wide variety of EEG artifacts based mainly on Template Matching approach including multiple signal-processing tools. The method was evaluated and validated on real EEG data, giving promising results that offer better capabilities to neurophysiologists in routine EEG examinations and diagnosis.

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“…Основная проблема электрофизиологических методов обследования -сильная зашумленность измерительных сигналов [3][4][5][6][7][8][9][10][11][12]. Эта проблема присуща и ЭГЭГ [5][6][7][8][9]: даже при строгом соблюдении протокола обследования интерпретация около 70 % записей затруднена из-за их зашумленности [8].…”

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Quality control of electrogastroenterography signals and measurement of gastric contractions rhythmicity

Попов¹

2017

Izv. vysš. učebn. zaved., Priborostr.

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Рассматривается проблема зашумленности данных электрофизиологических обследований пищеварительной системы человека. Представлен метод контроля качества записей электрогастроэнтерографии, основанный на анализе высокочастотных составляющих измерительных сигналов. Предложен количественный показатель качества измерительного сигнала электрогастроэнтерографии. Индикатор качества на основе данного показателя может использоваться непосредственно при проведении измерительных сеансов. Показан положительный эффект от использования предлагаемого подхода при измерении ритмичности сокращений желудка. Ключевые слова: цифровая обработка сигналов, артефакт, электрофизиология, электрогастроэнтерография, диагностика analysis of high-frequency components of measuring signals is presented. A quantitative indicator of measuring signal quality is proposed, the indicator may be used directly during the measurements recording. A positive effect of the proposed method as applied to measurement of gastric contractions rhythmicity is demonstrated.

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Automatic Artifact Removal in EEG Using Independent Component Analysis and One-Class Classification Strategy

Cited by 6 publications

References 0 publications

A Methodology for Enhancing SSVEP Features Using Adaptive Filtering Based on the Spatial Distribution of EEG Signals

A Methodology for Enhancing SSVEP Features Using Adaptive Filtering Based on the Spatial Distribution of EEG Signals

A Hybrid Approach for Artifacts Removal from EEG Recordings

Quality control of electrogastroenterography signals and measurement of gastric contractions rhythmicity

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