Automatic EEG artifact removal based on ICA and Hierarchical Clustering

Zou, Yuan; Hart, John; Jafari, Roozbeh

doi:10.1109/icassp.2012.6287967

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…A few proposals have been made along this line in previous works where two types of methods can be distinguished. The first exploit various statistical properties of the artifacts in the time-and frequencydomains [14,18], the second rely on prior knowledge about either the artifacts [9,19] or the signal of interest [21]. The former are specific to standard artifacts whereas the latter can integrate different kinds of prior information.…”

Section: Introductionmentioning

confidence: 99%

Non-negative matrix factorization for single-channel EEG artifact rejection

Damon

Liutkus

Gramfort

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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New applications of Electroencephalographic recording (EEG) pose new challenges in terms of artifact removal. In our work we target applications where the EEG is to be captured by a single electrode and a number of additional lightweight sensors are allowed. Thus, this paper introduces a new method for artifact removal for single-channel EEG recordings using nonnegative matrix factorisation (NMF) in a Gaussian source separation framework. We focus the study on ocular artifacts and show that by properly exploiting prior information on the latter, through the analysis of electrooculographic recordings, our artifact removal results on single-channel EEG are comparable to the results obtained with the classic multi-channel Independent Component Analysis technique.

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Section: Introductionmentioning

confidence: 99%

Non-negative matrix factorization for single-channel EEG artifact rejection

Damon

Liutkus

Gramfort

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…In order to achieve better classification performance, the bad trials need to be rejected in the preprocessing stage to improve the data quality. The most common method used for artifact removal is ICA [17]. However, ICA is not a good preprocessing candidate for realtime applications due to the high computation requirement.…”

Section: Preprocessing: Trial Rejectionmentioning

confidence: 99%

Score-based adaptive training for P300 speller Brain-Computer Interface

Zou

Dehzangi

Jafari

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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The primary aim of a Brain-Computer Interface (BCI) is to provide communication capabilities through brain signals recorded from the scalp for those with brain disorders to be able to interact with the outside world. In order to properly decode the electroencephalographic (EEG) brain signals, the BCI needs to adapt to the subject via calibration to ensure stable performance. One of the major challenges in realization of the EEG signals is the long calibration time required since they show significant variations between recording sessions even for the same subject within the same experimental condition. This paper proposes a score-based adaptive training algorithm that maximally utilizes relevant information from prior recording sessions and significantly shortens the calibration time. Also the proposed method is suitable to develop real-time, wearable, and low-power BCI embedded devices. The BCI developed in this work is based on the P300 word speller application introduced by Farwell and Donchin in 1988. The experimental results show that by employing few letters for calibration, the proposed adaptive training algorithm can achieve 100% classification accuracy.

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“…Thus, the inverse matrix (W -1 ) contains relative weights (spatial features) that denote components' source locations on the scalp topography [11]. Several studies have also used ICA technique for EEG artifact removal [21][22][23][24].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

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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Automatic EEG artifact removal based on ICA and Hierarchical Clustering

Cited by 12 publications

References 15 publications

Non-negative matrix factorization for single-channel EEG artifact rejection

Non-negative matrix factorization for single-channel EEG artifact rejection

Score-based adaptive training for P300 speller Brain-Computer Interface

A Hybrid Approach for Artifacts Removal from EEG Recordings

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