Accessible Electroencephalograms (EEGs): A Comparative Review with OpenBCI’s Ultracortex Mark IV Headset

Aldridge, Audrey; Barnes, Eli; Bethel, Cindy L.; Carruth, Daniel W.; Kocturova, Marianna; Pleva, Matúš; Juhar, Jozef

doi:10.1109/radioelek.2019.8733482

Cited by 29 publications

(20 citation statements)

References 5 publications

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“…To reduce the line noise artefact, we estimated the mains noise component in long segments of data and subtracted the same [32] before re-computing the PSD (see Methods for details), which yielded similar PSDs using both amplifiers (Fig 1B). The slopes of the PSDs computed at two different frequency ranges (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) Hz and 54-88 Hz as per our previous report [13]) were not significantly different between OpenBCI Visually similar results were obtained in the baseline-subtracted time frequency spectra of OpenBCI (first column) and Brain Products (second column). The change in power from baseline during stimulus at each frequency (Figure 3, third column), and the change in mean band power (in dB) of alpha, slow gamma and fast gamma bands with time (Fig 3 , 4th, 5th and 6th columns respectively) also showed visually similar trends.…”

Section: Baseline Psds and Slopes Are Comparablesupporting

confidence: 82%

“…We assessed the performance of OpenBCI, a popular affordable amplifier which provides a good cost-effectiveness [26], in the detection of gamma rhythms when static full-field gratings known to elicit gamma rhythms [12] were presented to healthy human subjects. OpenBCI recordings were compared to the recordings obtained using Brain Products GmbH, a popular research-grade amplifier, under identical experimental conditions on the same subjects.…”

Section: Introductionmentioning

confidence: 99%

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Stimulus-induced narrow-band gamma oscillations in humans can be recorded using open-hardware low-cost EEG amplifier

Pattisapu

Ray

2021

Preprint

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Stimulus-induced narrow-band gamma oscillations (30–70 Hz) in human electro - encephalograph (EEG) have been linked to attentional and memory mechanisms and are abnormal in mental health conditions such as autism, schizophrenia and Alzheimer’s Disease. This suggests that gamma oscillations could be valuable both as a research tool and an inexpensive, non-invasive biomarker for disease evaluation. However, since the absolute power in EEG decreases rapidly with increasing frequency following a “1/f” power law, and the gamma band includes line noise frequency, these oscillations are highly susceptible to instrument noise. Previous studies that recorded stimulus-induced gamma oscillations used expensive research-grade EEG amplifiers to address this issue. While low-cost EEG amplifiers have become popular in Brain Computer Interface applications that mainly rely on low-frequency oscillations (< 30 Hz) or steady-state-visually-evoked-potentials, whether they can also be used to measure stimulus-induced gamma oscillations is unknown. We recorded EEG signals using a low-cost, open-source amplifier (OpenBCI) and a traditional, research-grade amplifier (Brain Products GmbH) in male (N = 6) and female (N = 5) subjects (22–29 years) while they viewed full-screen static gratings that are known to induce gamma oscillations. OpenBCI recordings showed gamma response in almost all the subjects who showed a gamma response in Brain Products recordings, and the spectral and temporal profiles of these responses in alpha (8–13 Hz) and gamma bands were highly correlated between OpenBCI and Brain Products recordings. These results suggest that low-cost amplifiers can potentially be used in stimulus induced gamma response detection, making its research, and application in medicine more accessible.

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Section: Baseline Psds and Slopes Are Comparablesupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Stimulus-induced narrow-band gamma oscillations in humans can be recorded using open-hardware low-cost EEG amplifier

Pattisapu

Ray

2021

Preprint

View full text Add to dashboard Cite

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“…A low-cost, consumer-grade wearable EEG device would have channels ranging from 2 to 14 channels [ 58 ]. As seen from Figure 6 , the ease of setup while wearing a low-cost, consumer-grade wearable EEG headset provides comfort and reduces the complexity of setting up the device on the user's scalp, which is important for both researchers and users [ 63 ]. Even with the lower performance of wearable low-cost EEG devices, it is much more affordable compared to the standard clinical-grade EEG amplifiers [ 64 ].…”

Section: Emotionsmentioning

confidence: 99%

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Suhaimi

Mountstephens

Teo

2020

Computational Intelligence and Neuroscience

219

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Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful “emotional” interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

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“…For example, virtual industrial installations, such as the one presented in [35], and distant education, in particular aiming at the current efforts to develop so-called digital intelligence [36] or supporting people with special needs, such as autism spectrum disorder [37]. It is also planned to utilize LIRKIS G-CVE for future versions of the experiments described in [38,39], which focus on an assessment of VR influence on visuospatial cognitive functions [40]. Other applications, already under development, are training of civil engineering personnel, safety training and visualization of data from physical experiments in virtual environments.…”

Section: Discussionmentioning

confidence: 99%

Experimental Performance Evaluation of Enhanced User Interaction Components for Web-Based Collaborative Extended Reality

et al. 2021

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COVID-19-related quarantine measures resulted in a significant increase of interest in online collaboration tools. This includes virtual reality (VR) or, in more general term, extended reality (XR) solutions. Shared XR allows for activities such as presentations, training of personnel or therapy to take place in a virtual space instead of a real one. To make online XR as accessible as possible, a significant effort has been put into the development of solutions that can run directly in web browsers. One of the most recognized solutions is the A-Frame software framework, created by Mozilla VR team and supporting most of the contemporary XR hardware. In addition, an extension called Networked-Aframe allows multiple users to share virtual environments, created using A-Frame, in real time. In this article, we introduce and experimentally evaluate three components that extend the functionality of A-Frame and Networked-Aframe. The first one extends Networked-Aframe with the ability to monitor and control users in a shared virtual scene. The second one implements six degrees of freedom motion tracking for smartphone-based VR headsets. The third one brings hand gesture support to the Microsoft HoloLens holographic computer. The evaluation was performed in a dedicated local network environment with 5, 10, 15 and 20 client computers. Each computer represented one user in a shared virtual scene. Since the experiments were carried out with and without the introduced components, the results presented here can also be regarded as a performance evaluation of A-Frame and Networked-Aframe themselves.

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Accessible Electroencephalograms (EEGs): A Comparative Review with OpenBCI’s Ultracortex Mark IV Headset

Cited by 29 publications

References 5 publications

Stimulus-induced narrow-band gamma oscillations in humans can be recorded using open-hardware low-cost EEG amplifier

Stimulus-induced narrow-band gamma oscillations in humans can be recorded using open-hardware low-cost EEG amplifier

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Experimental Performance Evaluation of Enhanced User Interaction Components for Web-Based Collaborative Extended Reality

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