Review of Artifact Rejection Methods for Electroencephalographic Systems

Kanoga, Suguru; Mitsukura, Yasue

doi:10.5772/68023

Cited by 9 publications

(4 citation statements)

References 60 publications

(67 reference statements)

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“…Independent component analysis (ICA) as an accepted tool in artifacts rejection was used to eliminate nonbrain sources like eye blinks, muscle artifacts, and electrocardiogram (ECG). 43 The component rejection was applied manually according to this study. 44 In this study, 2 types of features, including complexity (LZC, FuzzyEn), and graph theory matrices (CC, degree), were investigated.…”

Section: Eeg Data Processingmentioning

confidence: 99%

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal

Mohammadi

Moradi

2020

Clin EEG Neurosci

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Background Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory–II scores). Methods Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal. Results Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band ( r = 0.839; P < .0001, root mean square error score of 7.69). Conclusion We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.

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Section: Eeg Data Processingmentioning

confidence: 99%

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal

Mohammadi

Moradi

2020

Clin EEG Neurosci

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“…While there also exist artifacts of technical nature, perhaps the most challenging artifacts in scalp EEG are of biological nature, namely, contamination by eye movements, by cardiac activity and in addition movement artifacts and EMG contamination produced by face and scalp muscles. While these artifacts can be quite large, there are now several approaches which can lead to relatively successful artifact handing ( Kaya, 2019 ) and artifact rejection ( Kanoga and Mitsukura, 2017 ). Identifying the true source of the oscillations from surface EEG recordings is a considerable challenge known as the inverse problem.…”

Section: Methodological Considerationsmentioning

confidence: 99%

Is there frequency-specificity in the motor control of walking? The putative differential role of alpha and beta oscillations

Charalambous

Hadjipapas²

2022

Front. Syst. Neurosci.

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Alpha and beta oscillations have been assessed thoroughly during walking due to their potential role as proxies of the corticoreticulospinal tract (CReST) and corticospinal tract (CST), respectively. Given that damage to a descending tract after stroke can cause walking deficits, detailed knowledge of how these oscillations mechanistically contribute to walking could be utilized in strategies for post-stroke locomotor recovery. In this review, the goal was to summarize, synthesize, and discuss the existing evidence on the potential differential role of these oscillations on the motor descending drive, the effect of transcranial alternate current stimulation (tACS) on neurotypical and post-stroke walking, and to discuss remaining gaps in knowledge, future directions, and methodological considerations. Electrophysiological studies of corticomuscular, intermuscular, and intramuscular coherence during walking clearly demonstrate that beta oscillations are predominantly present in the dorsiflexors during the swing phase and may be absent post-stroke. The role of alpha oscillations, however, has not been pinpointed as clearly. We concluded that both animal and human studies should focus on the electrophysiological characterization of alpha oscillations and their potential role to the CReST. Another approach in elucidating the role of these oscillations is to modulate them and then quantify the impact on walking behavior. This is possible through tACS, whose beneficial effect on walking behavior (including boosting of beta oscillations in intramuscular coherence) has been recently demonstrated in both neurotypical adults and stroke patients. However, these studies still do not allow for specific roles of alpha and beta oscillations to be delineated because the tACS frequency used was much lower (i.e., individualized calculated gait frequency was used). Thus, we identify a main gap in the literature, which is tACS studies actually stimulating at alpha and beta frequencies during walking. Overall, we conclude that for beta oscillations there is a clear connection to descending drive in the corticospinal tract. The precise relationship between alpha oscillations and CReST remains elusive due to the gaps in the literature identified here. However, better understanding the role of alpha (and beta) oscillations in the motor control of walking can be used to progress and develop rehabilitation strategies for promoting locomotor recovery.

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“…This method can cause a significant loss of information as the EEG segments being removed may contain useful neurological information. Alternatively, many semi-automatic and fully-automatic eyeblink artifact removal algorithms were developed to replace manual eyeblink artifact rejection method, which were discussed and reviewed in [4,[18][19][20][21][22]. In addition to the existing automatic artifact removal algorithms, techniques addressing and implementing online removal of eyeblink artifacts are essential [20].…”

Section: Introductionmentioning

confidence: 99%