Surrogate-Based Artifact Removal From Single-Channel EEG

Chávez, Mario; Grosselin, Fanny; Bussalb, Aurore; Fallani, F. De Vico; Navarro-Sune, Xavier

doi:10.1109/tnsre.2018.2794184

Cited by 88 publications

(61 citation statements)

References 40 publications

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“…At signal processing level, it would be convenient to implement some method for physiological artifact identification and removal in EEG registers (see [57] for a review). Consequently, it is intended to evaluate the detection capacity of GBA using artifact reduction techniques in single-channel acquisition systems, designed to eliminate some particular type of interference (e.g., ocular movements [58]) or more generalists ones [59].…”

Section: Discussionmentioning

confidence: 99%

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Usanos

Boquete

Santiago

et al. 2020

Sensors

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The purpose of this paper is to record and analyze induced gamma-band activity (GBA) (30–60 Hz) in cerebral motor areas during imaginary movement and to compare it quantitatively with activity recorded in the same areas during actual movement using a simplified electroencephalogram (EEG). Brain activity (basal activity, imaginary motor task and actual motor task) is obtained from 12 healthy volunteer subjects using an EEG (Cz channel). GBA is analyzed using the mean power spectral density (PSD) value. Event-related synchronization (ERS) is calculated from the PSD values of the basal GBA (GBAb), the GBA of the imaginary movement (GBAim) and the GBA of the actual movement (GBAac). The mean GBAim and GBAac values for the right and left hands are significantly higher than the GBAb value (p = 0.007). No significant difference is detected between mean GBA values during the imaginary and actual movement (p = 0.242). The mean ERS values for the imaginary movement (ERSimM (%) = 23.52) and for the actual movement (ERSacM = 27.47) do not present any significant difference (p = 0.117). We demonstrated that ERS could provide a useful way of indirectly checking the function of neuronal motor circuits activated by voluntary movement, both imaginary and actual. These results, as a proof of concept, could be applied to physiology studies, brain–computer interfaces, and diagnosis of cognitive or motor pathologies.

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Section: Discussionmentioning

confidence: 99%

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Usanos

Boquete

Santiago

et al. 2020

Sensors

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“…Preprocessing of EEG signals is required to remove noise and can be achieved by converting a multiple channel EEG signal into a surrogate channel [55,56]…”

Section: Preprocessingmentioning

confidence: 99%

Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies

Usman

Khalid

Akhtar

et al. 2019

Seizure

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Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a seizure before it occurs may prove useful as patients might be alerted to make themselves safe or seizures could be prevented with therapeutic interventions just before they occur. Abnormal neuronal activity, the preictal state, starts a few minutes before the onset of a seizure. In recent years, different methods have been proposed to predict the start of the preictal state. These studies follow some common steps, including recording of EEG signals, preprocessing, feature extraction, classification, and postprocessing. However, online prediction of epileptic seizures remains a challenge as all these steps need further refinement to achieve high sensitivity and low false positive rate. In this paper, we present a comparison of state-of-the-art methods used to predict seizures using both scalp and intracranial EEG signals and suggest improvements to

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“…All the signals were filtered with a 38.5 Hz Low Pass Notch Filter incorporated into the series amplifier modules of BioPac, thereby eliminating the 50 Hz mains interference. Two differentiated signals (FP1 minus AF7 and FP2 minus AF8) were obtained and filtered using a recent datadriven algorithm that removes ocular and muscle artifacts from the single-channel data, referred to as the surrogate-based artifact removal (SuBAR) method [17]. Although the full details are given elsewhere [17], the algorithm follows the pipeline: 1, Z-score of the data; 2, Maximal Overlap Wavelet Transform (MODWT) of the data, using symlets of order 5 and with 5 levels of decomposition; 3, Removal of artifacts, defined as the values of the wavelet coefficients that are outliers relative to the distribution of the values obtained from the data surrogates (to identify outliers, we considered a 5% significance level and the outlier coefficients were eliminated by substituting their values with the average coefficients obtained from the surrogates); 4.…”

Section: Capture and Cleaning Of Physiological Signalsmentioning

confidence: 99%

A controlled thermoalgesic stimulation device to identify novel pain perception biomarkers

Rivero

Camino

Diez

et al. 2020

Preprint

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AbstractObjectiveTo develop a new device that will help identify physiological markers of pain perception by reading the brain’s electrical activity and the bodies hemodynamic interactions while applying thermoalgesic stimulation. Methods: We designed a compact prototype that generates well-controlled thermal stimuli using a computer driven Peltier cell while simultaneously capturing electroencephalography (EEG) and photoplethysmography (PPG) signals as the stimuli are varied. The study was performed on 35 healthy subjects (mean age 30.46 years, SD 4.93 years; 20 males, 15 females) and to account for the inter-subject variability in the tolerance to thermal pain, we first determined the heat pain threshold (HPT) for each subject, defined as the maximum temperature that the subject can withstand when the Peltier cell gradually increases the temperature. Subsequently, we defined the pain parameters associated with a stimulation temperature equivalent to 90% of the HPT, comparing this to the no-pain state (control) in the absence of thermoalgesic stimulation. Results: Both the one-dimensional and the two-dimensional spectral entropy (SE) obtained from both the EEG and PPG signals could differentiate the condition of pain. In particular, the PPG SE was significantly reduced in association with pain, while the SE for EEG increased slightly. Moreover, significant discrimination occurred within a specific range of frequencies, 26-30 Hz for EEG and about 5-10 Hz for PPG. Conclusion: Hemodynamics, brain dynamics and their interactions can discriminate thermal pain perception. Significance: The possibility of monitoring on-line variations in thermal pain perception using a similar device and algorithms may be of interest to study different pathologies that affect the peripheral nervous system, such as small fiber neuropathies, fibromyalgia or painful diabetic neuropathy.

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Surrogate-Based Artifact Removal From Single-Channel EEG

Abstract: in view of these results, the SuBAR method is a promising solution for mobile environments, such as ambulatory healthcare systems, sleep stage scoring, or anesthesia monitoring, where very few EEG channels or even a single channel is available.

Cited by 88 publications

References 40 publications

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies

A controlled thermoalgesic stimulation device to identify novel pain perception biomarkers

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