Artifacts and noise removal for electroencephalogram (EEG): A literature review

Lai, Chi Qin; Ibrahim, Haidi; Abdullah, Mohd Zaid; Abdullah, Jafri Malin; Suandi, Shahrel Azmin; Azman, Azlinda

doi:10.1109/iscaie.2018.8405493

Cited by 39 publications

(23 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…EEG data contain Ocular (eye blink) and Myogenic artefacts as noise. Several techniques, including Independent Component Analysis (ICA), Statistical Analysis, and Wavelet-Based Analysis, are used for noise removal [8] [9]. For feature extraction, techniques such as Principal Component Analysis (PCA) [10]and Discrete Wavelet Transform (DWT) [11] are widely used.…”

Section: Introductionmentioning

confidence: 99%

An EEG based Channel Optimized Classification Approach for Autism Spectrum Disorder

Haputhanthri

Brihadiswaran

Gunathilaka

et al. 2019

2019 Moratuwa Engineering Research Conference (MERCon)

View full text Add to dashboard Cite

Spectrum Disorder (ASD) is a neurodevelopmental condition which affects a persons cognition and behaviour. It is a lifelong condition which cannot be cured completely using any intervention to date. However, early diagnosis and follow-up treatments have a major impact on autistic people. Unfortunately, the current diagnostic practices, which are subjective and behaviour dependent, delay the diagnosis at an early age and makes it harder to distinguish autism from other developmental disorders. Several works of literature explore the possible behaviour-independent measures to diagnose ASD. Abnormalities in EEG can be used as reliable biomarkers to diagnose ASD. This work presents a low-cost and straightforward diagnostic approach to classify ASD based on EEG signal processing and learning models. Possibilities to use a minimum number of EEG channels have been explored. Statistical features are extracted from noise filtered EEG data before and after Discrete Wavelet Transform. Relevant features and EEG channels were selected using correlation-based feature selection. Several learning models and feature vectors have been studied and possibilities to use the minimum number of EEG channels have also been explored. Using Random Forest and Correlation-based Feature Selection, an accuracy level of 93% was obtained.

show abstract

Section: Introductionmentioning

confidence: 99%

An EEG based Channel Optimized Classification Approach for Autism Spectrum Disorder

Haputhanthri

Brihadiswaran

Gunathilaka

et al. 2019

2019 Moratuwa Engineering Research Conference (MERCon)

View full text Add to dashboard Cite

show abstract

“…Filtering for Noise removal: Conventionally, EEG signal is divided into various band of frequencies namely delta(δ), theta(θ), alpha(α), beta(β) and gamma(γ) with frequency range of 0-4 Hz, 4-8 Hz, 8-13 Hz, 13-30 Hz and above 30 Hz, respectively. To retain the useful information and for removal of noise [27,28], as per AASM criterion [12], we have band-pass filtered the EEG signal using a finite impulse response (FIR) filter with Kaiser window. Only frequencies above 1 Hz and below 35 Hz were retained in our study.…”

Section: Preprocessingmentioning

confidence: 99%

Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals

et al. 2021

View full text Add to dashboard Cite

A sleep disorder is a medical condition that affects an individual’s regular sleeping pattern and routine, hence negatively affecting the individual’s health. The traditional procedures of identifying sleep disorders by clinicians involve questionnaires and polysomnography (PSG), which are subjective, time-consuming, and inconvenient. Hence, an automated sleep disorder identification is required to overcome these limitations. In the proposed study, we have proposed a method using electroencephalogram (EEG) signals for the automated identification of six sleep disorders, namely insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, rapid eye movement behavior disorder (RBD), periodic leg movement disorder (PLM), and sleep-disordered breathing (SDB). To the best of our belief, this is one of the first studies ever undertaken to identify sleep disorders using EEG signals employing cyclic alternating pattern (CAP) sleep database. After sleep-scoring EEG epochs, we have created eight different data subsets of EEG epochs to develop the proposed model. A novel optimal triplet half-band filter bank (THFB) is used to obtain the subbands of EEG signals. We have extracted Hjorth parameters from subbands of EEG epochs. The selected features are fed to various supervised machine learning algorithms for the automated classification of sleep disorders. Our proposed system has obtained the highest accuracy of 99.2%, 98.2%, 96.2%, 98.3%, 98.8%, and 98.8% for insomnia, narcolepsy, NFLE, PLM, RBD, and SDB classes against normal healthy subjects, respectively, applying ensemble boosted trees classifier. As a result, we have attained the highest accuracy of 91.3% to identify the type of sleep disorder. The proposed method is simple, fast, efficient, and may reduce the challenges faced by medical practitioners during the diagnosis of various sleep disorders accurately in less time at sleep clinics and homes.

show abstract

“…Two important assumptions are made in ICA: (1) the signals from different sources are independent of each other and (2) independent components have non-gaussian distribution. Artefact removal in EEG signals using ICA is a three-step process: (1) decomposing into ICs, (2) discarding standalone ICs and (3) concatenating the remaining ICs to form an artefact-free signal (Lai et al, 2018).…”

Section: Independent Component Analysismentioning

confidence: 99%

“…The studies done in (Khatwani and Tiwari, 2013;Urigüen and Garcia-Zapirain, 2015;Lai et al, 2018) have presented surveys of denoising techniques. Khatwani and Tiwari (2013) have discussed denoising techniques based on PCA, ICA, wavelet and wavelet packet in their work.…”

Section: Visual Inspectionmentioning

confidence: 99%

See 1 more Smart Citation

EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Brihadiswaran¹,

Haputhanthri²,

Gunathilaka³

et al. 2019

Journal of Computer Science

View full text Add to dashboard Cite

Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents a survey of major EEG-based ASD classification approaches from 2010 to 2018, which adopt machine learning. The methodology is divided into four phases: EEG data collection, pre-processing, feature extraction and classification. This study explores different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model. We analyze the strengths and weaknesses of the techniques and tools. Further, this study summarizes the ASD classification approaches and discusses the existing challenges, limitations and future directions.

show abstract

Artifacts and noise removal for electroencephalogram (EEG): A literature review

Cited by 39 publications

References 34 publications

An EEG based Channel Optimized Classification Approach for Autism Spectrum Disorder

An EEG based Channel Optimized Classification Approach for Autism Spectrum Disorder

Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals

EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Contact Info

Product

Resources

About