Interactive Visualization of the Cranio-Cerebral Correspondences for 10/20, 10/10 and 10/5 Systems

Silva, José Angel Iván Rubianes; Burgos, Fabio Enrique Suarez; Wu, Shin-Ting

doi:10.1109/sibgrapi.2016.065

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…We took advantage of a pre‐existing neuroimaging dataset taken from a combined EEG and functional magnetic resonance imaging (fMRI) experiment, using 64 channel fixed electrode caps with a 10‐10 electrode layout (Scrivener et al., 2021 ). Whilst several groups have developed methods to recover EEG electrode positions from simultaneous EEG‐fMRI data using specific MRI acquisition methods (Butler et al., 2017 ) or reconstruction from acquired structural scans (Bhutada et al., 2020 ; Brinkmann et al., 1998 ; de Munck et al., 2012 ; Jurcak et al., 2005 ; Koessler et al., 2008 ; Kozinska et al., 2001 ; Lamm et al., 2001 ; Marino et al., 2016 ; Silva et al., 2016 ; Whalen et al., 2008 ), these approaches often require methods and toolboxes that are not yet widely used. As such, we additionally highlight a novel and simple way of projecting electrode locations to the cortical surface using electrode gel artifacts (that appear on the MR image underlying electrode positions) and commercially available equipment.…”

Section: Introductionmentioning

confidence: 99%

Variability of EEG electrode positions and their underlying brain regions: visualizing gel artifacts from a simultaneous EEG‐fMRI dataset

Scrivener

Reader

2022

Brain and Behavior

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Introduction:We investigated the between-subject variability of EEG (electroencephalography) electrode placement from a simultaneously recorded EEG-fMRI (functional magnetic resonance imaging) dataset.Methods: Neuro-navigation software was used to localize electrode positions, made possible by the gel artifacts present in the structural magnetic resonance images. To assess variation in the brain regions directly underneath electrodes we used MNI coordinates, their associated Brodmann areas, and labels from the Harvard-Oxford Cortical Atlas. We outline this relatively simple pipeline with accompanying analysis code. Results:In a sample of 20 participants, the mean standard deviation of electrode placement was 3.94 mm in x, 5.55 mm in y, and 7.17 mm in z, with the largest variation in parietal and occipital electrodes. In addition, the brain regions covered by electrode pairs were not always consistent; for example, the mean location of electrode PO7 was mapped to BA18 (secondary visual cortex), whereas PO8 was closer to BA19 (visual association cortex). Further, electrode C1 was mapped to BA4 (primary motor cortex), whereas C2 was closer to BA6 (premotor cortex).Conclusions: Overall, the results emphasize the variation in electrode positioning that can be found even in a fixed cap. This may be particularly important to consider when using EEG positioning systems to inform non-invasive neurostimulation.

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Section: Introductionmentioning

confidence: 99%

Variability of EEG electrode positions and their underlying brain regions: visualizing gel artifacts from a simultaneous EEG‐fMRI dataset

Scrivener

Reader

2022

Brain and Behavior

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“…Figures 15(b The test setup of the first Test subject-1 is shown in Figure . 15(a). The subject is wearing the headwear making sure that the frontal EEG electrode is placed precisely at FP1 [17,27]. Before electrode placement, the skin at FP1 is cleaned with cotton to reduce skin impedance.…”

Section: Test Resultsmentioning

confidence: 99%

“…The electrodes that need to be placed on the scalp of the head are followed by an internationally recognized standard known as the 10-20 system [17]. This system is strictly in relation to the location of the electrodes and the cerebral cortex.…”

Section: The 10-20 System Of Electrode Placementmentioning

confidence: 99%

“…This module is integrated with dry silver electrodes and a Bluetooth module which makes up the components of the headwear. The electrodes pick up differences in electric potential at FP1 [17] and the TGAM pre-processes this signal and the Bluetooth module transmits this signal to a computer.…”

Section: Design Of Brain Controlled Wheelchair 31 Design Approachmentioning

confidence: 99%

“…The TGAM module is powered by a battery pack that supplies a cumulative voltage of 4.5V. A single channel non-invasive silver EEG electrode placed at the pre-frontal lobe (FP1) [17,27,28] extracts electrical signals from the scalp of the subject. This particular FP1 placement is used because FP1 and FP2 placements show the greatest deflection of potential in a time-domain EEG waveform [29].…”

Section: Headwearmentioning

confidence: 99%

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Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

Francis

Umayal

Kanimozhi

2021

IJEEI

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The human brain is considered one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. An embedded Bluetooth module acquires and transfers the signals wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in realtime and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI. The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The wheelchair attained a command detection and execution accuracy of 96.4% which is an improvement from the existing systems. The work presented here gives a basic understanding of the functionality of a BCI system and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis.

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A Swarm Intelligence Approach: Combination of Different EEG-Channel Optimization Techniques to Enhance Emotion Recognition

Balic

Kleybolte

Märtin

2022

Human-Computer Interaction. Technological Innovation

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Interactive Visualization of the Cranio-Cerebral Correspondences for 10/20, 10/10 and 10/5 Systems

Cited by 9 publications

References 11 publications

Variability of EEG electrode positions and their underlying brain regions: visualizing gel artifacts from a simultaneous EEG‐fMRI dataset

Variability of EEG electrode positions and their underlying brain regions: visualizing gel artifacts from a simultaneous EEG‐fMRI dataset

Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

A Swarm Intelligence Approach: Combination of Different EEG-Channel Optimization Techniques to Enhance Emotion Recognition

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