Removal of high-voltage brain stimulation artifacts from simultaneous EEG recordings

Lee, S.; Wang, Z.J.; McKeown, Martin J.

doi:10.1016/j.brs.2017.01.458

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of simulated/synthetic EEG+tACS data, modelling the tACS interference as as an additive noise source is the second most common. Most of these models include some second order effects such as timing jitter, however the simple additive tACS model is very debatable [16], [17]. The majority of works use more than one validation approach in order to help build confidence in the artefact removal procedure.…”

Section: Discussionmentioning

confidence: 99%

Machine learning validation of EEG+tACS artefact removal

Kohli

Casson

2020

J. Neural Eng.

View full text Add to dashboard Cite

Motivation: Electroencephalography (EEG) recorded during Transcranial Alternating Current Simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artefact removal algorithms have been presented previously, but with substantial debates on their performance, utility, and the presence of any residual artefacts. This paper investigates whether machine learning can be used to validate artefact removal algorithms. The postulation is that residual artefacts in the EEG after cleaning would be independent of the experiment performed, making it impossible to differentiate between different parts of an EEG+tACS experiment, or between different behavioural tasks performed. Methods: Ten participates undertook two tasks (nBack and backwards digital recall) during simultaneous EEG+tACS, exercising different aspects of working memory. Stimulation during no task and sham conditions were also performed. A previously reported tACS artefact removal algorithm from our group was used to clean the EEG and a Linear Discriminant Analysis was trained on the cleaned EEG to differentiate different parts of the experiment. Results: Baseline, baseline during tACS, working memory task without tACS, and working memory task with tACS data segments could be differentiated with accuracies ranging from 65-94%, far exceeding chance levels. EEG from the nBack and backwards digital recall tasks could be separated during stimulation, with an accuracy exceeding 72%. If residual tACS artefacts remained after the EEG cleaning these did not dominate the classification process. Significance: This helps in building confidence that true EEG information is present after artefact removal. Our methodology presents a new approach to validating tACS artefact removal approaches.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning validation of EEG+tACS artefact removal

Kohli

Casson

2020

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

“…In order to read this information it is necessary an Electroencephalography, which is a technique that records the electrical activity of neurons in a region called the brain, consisting of the brain, cerebellum and brainstem, and the spinal cord, which makes up the central nervous system, Figure 5 [3], [12], [13], [14], [4].…”

Section: A Obtaining Data By Brain Signalsmentioning

confidence: 99%

Help Tetraplegic People by Means of a Computational Neuronal Control System

Moreno¹,

Matamoros²,

Tejeida

et al. 2019

ijacsa

View full text Add to dashboard Cite

In the present document we present an Interface called BrainMouse where its main task is to help people with motor disabilities specially tetraplegic or quadriplegic people that they can move the mouse of the computer by means of blinking or any neural response. The Interface uses the data obtained from a neuronal system which is responsible for taking reliable readings of the electrical signals generated in the human brain, through non-intrusive neuronal interfaces. The recorded data is used by the BrainMouse Interface so that the mouse can perform functions such as an up, down, left lateral, right lateral, left click, right click and double click. Thus, this interface has all the options that a conventional mouse would have.

show abstract

“…K. Erikson, J. Laurila, J. Koskenkari and T. Ala-Kokko are with the Research Group of Surgery, Anaesthesiology and Intensive Care, Medical Faculty, University of Oulu, Oulu, Finland and with the Division of Intensive substantially present in the recordings of critically ill patients due to the long duration of the measurement and challenges related to the environment such as the presence of electromechanical devices as well as motion artifacts arising from both staff and patient [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG

Bahador

Erikson²,

Laurila³

et al. 2020

J. Neural Eng.

View full text Add to dashboard Cite

Objective: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance.Approach: A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact.Main results: The method with a classification accuracy of 92.30% (AUC=0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset.Significance: This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.

show abstract

Removal of high-voltage brain stimulation artifacts from simultaneous EEG recordings

Cited by 3 publications

References 0 publications

Machine learning validation of EEG+tACS artefact removal

Machine learning validation of EEG+tACS artefact removal

Help Tetraplegic People by Means of a Computational Neuronal Control System

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG

Contact Info

Product

Resources

About