EEG mobility artifact removal for ambulatory epileptic seizure prediction applications

Islam, Sirajul; El-Hajj, Ahmad M.; Alawieh, Hussein; Dawy, Zaher; Abbas, Nabil; El-Imad, Jamil

doi:10.1016/j.bspc.2019.101638

Cited by 39 publications

(15 citation statements)

References 30 publications

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“…In conclusion, we can add to the topic of artefacts in the EEG that the elimination of artefacts is not regulated in any way and the causes of artefacts are great and their nature and behaviour may depend on various external and internal factors. Islam et al [35], Levitt et al [36], Yang et al [37], Dhindsa et al [38], and Abdulla et al [39] presented the similar research with the different neural network model and its configuration. But the conclusions obtained as a result, as a rule, have a similar explanation.…”

Section: Neural Network For Control Artifact In Eeg Signalsmentioning

confidence: 78%

Review of Neural Networks in the EEG Signal Recognition

Rakhmatulin

2020

SSRN Journal

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In the last decade, unprecedented progress in the development of neural networks influenced dozens of different industries, among which are signal processing for the electroencephalography process (EEG). Electroencephalography, even though it appeared in the first half of the 20th century, to this day didn't change the physical principles of operation. But the signal processing technique due to the use of neural networks progressed significantly in this area. Evidence for this can serve that for the past 5 years more than 1000 publications on the topic of using machine learning have been published in popular libraries. Many different models of neural networks complicate the process of understanding the real situation in this area. In this manuscript, we provided the most comprehensive overview of research where were used neural networks for EEG signal processing.

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Section: Neural Network For Control Artifact In Eeg Signalsmentioning

confidence: 78%

Review of Neural Networks in the EEG Signal Recognition

Rakhmatulin

2020

SSRN Journal

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“…At present, the blind source separation algorithm (BSS) is used to separate the EEG and EMG sources into different components, and then remove the muscle-related components during the reconstruction process. BSS techniques mainly include independent component analysis (ICA) [32] , [33] , canonical correlation analysis (CCA) [34] and independent vector analysis (IVA) [35] . One fundamental requirement of the above multichannel techniques is that the number of channels must be larger than or equal to the number of underlying sources.…”

Section: Methods and Proceduresmentioning

confidence: 99%

Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

Fan

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Objective: Most of effectiveness assessments of the widely-used Massage therapy were based on subjective routine clinical assessment tools, such as Visual Analogue Scale (VAS) score. However, few studies demonstrated the impact of massage on the Electroencephalograph (EEG) rhythm decoding of Motor imagery (MI) and motion execution (ME) with trunk left/right bending in patients with skeletal muscle pain. Method: We used the sample entropy (SampEn), permutation entropy (PermuEn), common spatial pattern (CSP) features, support vector machine (SVM) and logic regression (LR) classifiers. We also used the convolutional neural network (CNN) and attention-based bi-directional long short-term memory (BiLSTM) for classification. Results: The averaged SampEn and PermuEn values of alpha rhythm decreased in almost fourteen channels for five statuses (quiet, MI with left/right bending, ME with left/right bending). It indicated that massage alleviates the pain for the patients of skeletal pain. Furthermore, compared with the SVM and LR classifiers, the BiLSTM method achieved a better area under curve (AUC) of 0.89 for the classification of MI with trunk left/right bending before massage. The AUC became smaller after massage than that before massage for the classification of MI with trunk left/right bending using CNN and BiLSTM methods. The Permutation direct indicator (PDI) score showed the significant difference for patients in different statuses (before vs after massage, and MI vs ME). Conclusions: Massage not only affects the quiet status, but also affects the MI and ME. Clinical Impact: Massage therapy may affect a bit on the accuracy of MI with trunk left/right bending and it change the topography of MI and ME with trunk left/right bending for the patients with skeletal muscle pain.

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“…Motions, electrophysiological signals excluding EEG, and electromagnetic interference, such as various sources, can cause artifacts. In both clinical and daily life applications, artifacts are a major hurdle in the interpretation of EEG signal since artifacts reduce the accuracy of automated classification of signal sequences for clinical diagnosis (Islam et al, 2020) and disturb the operation of the BCI system (Guarnieri et al, 2018). In particular, motion artifacts in wearable devices have been a key challenge due to the inevitable body and device movements.…”

Section: Electroencephalography Motion Artifact Removalmentioning

confidence: 99%

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

et al. 2021

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Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.

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EEG mobility artifact removal for ambulatory epileptic seizure prediction applications

Cited by 39 publications

References 30 publications

Review of Neural Networks in the EEG Signal Recognition

Review of Neural Networks in the EEG Signal Recognition

Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

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