Methods for artifact detection and removal from scalp EEG: A review

Islam, Kafiul; Rastegarnia, Amir; Yang, Zhi

doi:10.1016/j.neucli.2016.07.002

Cited by 343 publications

(221 citation statements)

References 84 publications

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“…Electromyography artifacts were removed automatically using the AAR external plug-in with the default 30-sec sliding window [40, 41], and 60-Hz line noise harmonics (50 Hz for Oslo data) were removed using discrete Fourier transform. Then, we epoched the continuous data into trials with 1000-msec buffers, excluded any trials that had been marked based on eyegaze position, and manually inspected the data to reject any channels containing abnormal signal.…”

Section: Star Methodsmentioning

confidence: 99%

Bidirectional Frontoparietal Oscillatory Systems Support Working Memory

et al. 2017

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Summary The ability to represent and select information in working memory provides the neurobiological infrastructure for human cognition. For 80 years, dominant views of working memory have focused on the key role of prefrontal cortex (PFC) [1–8]. However, more recent work has implicated posterior cortical regions [9–12], suggesting that PFC engagement during working memory is dependent on the degree of executive demand. We provide evidence from neurological patients with discrete PFC damage that challenges the dominant models attributing working memory to PFC-dependent systems. We show that neural oscillations, which provide a mechanism for PFC to communicate with posterior cortical regions [13], independently subserve communications both to and from PFC – uncovering parallel oscillatory mechanisms for working memory. Fourteen PFC patients and 20 healthy, age-matched controls performed a working memory task where they encoded, maintained, and actively processed information about pairs of common shapes. In controls, the electroencephalogram (EEG) exhibited oscillatory activity in the low-theta range over PFC and directional connectivity from PFC to parieto-occipital regions commensurate with executive processing demands. Concurrent alpha-beta oscillations were observed over parieto-occipital regions, with directional connectivity from parieto-occipital regions to PFC, regardless of processing demands. Accuracy, PFC low-theta activity, and PFC ➔ parieto-occipital connectivity were attenuated in patients, revealing a PFC-independent, alpha-beta system. The PFC patients still demonstrated task proficiency, which indicates that the posterior alpha-beta system provides sufficient resources for working memory. Taken together, our findings reveal neurologically dissociable PFC and parieto-occipital systems, and suggest that parallel, bidirectional oscillatory systems form the basis of working memory.

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Section: Star Methodsmentioning

confidence: 99%

Bidirectional Frontoparietal Oscillatory Systems Support Working Memory

et al. 2017

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“…Importantly, there is no method designed specifically for single-channel recordings in rodents. 36,37 When artifact identification is applied in a seizure detection algorithm, there is a high risk of erroneously identifying a seizure as an artifact, especially if the seizure is contaminated by artifacts, as occurs normally in chronic EEG recordings and we observed several times in our recordings. 38 Therefore, we decided to use a reliable and conservative procedure for artifact identification.…”

Section: Discussionmentioning

confidence: 73%

“…A variety of artifact detection and removal methods of different complexity are described in literature, although no single existing artifact detection method is universal. Importantly, there is no method designed specifically for single‐channel recordings in rodents …”

Section: Discussionmentioning

confidence: 99%

A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy

Casillas‐Espinosa

Sargsyan²,

Melkonian³

et al. 2019

Epilepsia

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Summary Objective Prolonged electroencephalographic (EEG) monitoring in chronic epilepsy rodent models has become an important tool in preclinical drug development of new therapies, in particular those for antiepileptogenesis, disease modification, and treating drug‐resistant epilepsy. We have developed an easy‐to‐use, reliable, computational tool for automated detection of electrographic seizures from prolonged EEG recordings in rodent models of epilepsy. Methods We applied a novel method based on advanced time‐frequency analysis that detects EEG episodes with excessive activity in certain frequency bands. The method uses an innovative technique of short‐term spectral analysis, the Similar Basis Function algorithm. The method was applied for offline seizure detection from long‐term EEG recordings from four spontaneously seizing, chronic epilepsy rat models: the fluid percussion injury (n = 5 rats, n = 49 seizures) and post–status epilepticus models (n = 119 rats, n = 993 seizures) of acquired epilepsy, and two genetic models of absence epilepsy, Genetic Absence Epilepsy Rats from Strasbourg and Wistar Albino Glaxo from Rijswijk (n = 41 and 14 rats, n = 8733 and 825 seizures, respectively). Results Our comparative analysis revealed that the EEG amplitude spectra of these four rat models are remarkably similar during epileptiform activity and have a single expressed peak within the 17‐ to 25‐Hz frequency range. Focusing on this band, our computer program detected all seizures in the 179 rats. A quick semiautomated user inspection of the EEGs for the period of each identified event allowed quick rejection of artifact events. The overall processing time for 12‐day‐long recordings varied from a few minutes (5‐10) to 30 minutes, depending on the number of artifact events, which was strongly correlated with the signal quality of the raw EEG data. Significance Our automated seizure detection tool provides high sensitivity, with acceptable specificity, for long‐ and short‐term EEG recordings from both acquired and genetic chronic epilepsy rat models. This tool has the potential to improve the efficiency and rigor of preclinical research and therapy development using these models.

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“…In reviewing the literature on artifact detection and removal, Islam et al [42] concluded that none of the existing methods is a perfect solution. One problem is that it is often difficult to evaluate the effectiveness of the methods because completely uncontaminated data may not be available.…”

Section: Eeg Analysis For Bcismentioning

confidence: 99%

EEG-based brain–computer interfaces

McFarland

Wolpaw

2017

Current Opinion in Biomedical Engineering

198

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Brain-Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more confortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.

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Methods for artifact detection and removal from scalp EEG: A review

Cited by 343 publications

References 84 publications

Bidirectional Frontoparietal Oscillatory Systems Support Working Memory

Bidirectional Frontoparietal Oscillatory Systems Support Working Memory

A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy

EEG-based brain–computer interfaces

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