Consumer Grade EEG Measuring Sensors as Research Tools: A Review

Sawangjai, Phattarapong; Hompoonsup, Supanida; Leelaarporn, Pitshaporn; Kongwudhikunakorn, Supavit; Wilaiprasitporn, Theerawit

doi:10.1109/jsen.2019.2962874

Cited by 186 publications

(132 citation statements)

References 205 publications

Supporting

Mentioning

123

Contrasting

Unclassified

Order By: Relevance

“…While a direct comparison of our functional connectivity results against other studies is not possible, our signal-tonoise ratio and power spectra and topography results are consistent with other studies, lending credibility to our analysis. In a recent review of "consumer grade" EEG systems as research tools, Sawangjai et al reviewed studies from the past five years that used wearable EEG devices as the primary equipment for data collection [7]. Similarly to our findings, reviews of studies that used the Emotiv EPOC+ concluded that data quality, sensor location and electrode number posed considerable challenges when comparing this headset to "medical grade devices", while the OpenBCI was more comparable to research grade devices on the basis of signal quality and spectral properties.…”

Section: Discussionmentioning

confidence: 99%

“…Although EEG is typically recorded by sophisticated medical instruments operated by professionally trained experts, there has been a recent proliferation of portable and affordable single-and multi-channel EEG devices marketed for personal and everyday applications [1]- [6]. These devicesreferred to as consumer-grade EEG, mobile EEG, translational EEG and wearable EEGhave become increasingly popular due to their low cost, their ease of setup and their relatively acceptable performance [7]. To date, they have been used in applications ranging from clinical monitoring (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Quality of Wearable EEG Systems Using Functional Connectivity

2020

View full text Add to dashboard Cite

Assessing the data quality of wearable electroencephalogram (EEG) systems is critical to collecting reliable neurophysiological data in non-laboratory environments. To date, measures of signal quality and spectral characteristics have been used to characterize wearable EEG systems. We demonstrate that these traditional measures do not provide fine-grained differentiation between the performance of four popular wearable EEG systems (the Epoc+, OpenBCI, DSI-24 and Quick-30 Dry EEG). Using two computationally inexpensive metrics of undirected functional connectivity (phase lag index) and directed functional connectivity (directed phase lag index), we compare the integrity of the phase relationships captured by wearable systems to those recorded from a high-density research-grade EEG system (Electrical Geodesics Inc). Our results demonstrate that functional connectivity analyses provide additional discriminatory information about wearable EEG systems, with clear differentiation of the cosine similarity between research-grade functional connectivity patterns and those generated by each wearable system. We provide a freely available Matlab toolbox containing all metrics described in this paper such that researchers and non-experts interested in wearable EEG systems can easily assess the quality of systems not characterized in this study, thus advancing the translation of EEG research into non-laboratory settings.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Quality of Wearable EEG Systems Using Functional Connectivity

2020

View full text Add to dashboard Cite

show abstract

“…Various inexpensive devices were tested to measure and analyze EEG recordings [ 110 , 111 , 112 , 113 ]. One of the first commercial, inexpensive devices applied for EEG was a headset from NeuroSky Mind-Wave, which was supposed to capture different levels of EEG [ 114 , 115 ].…”

Section: Overview Of Already Existing Methodsmentioning

confidence: 99%

“…Among such systems are the above mentioned Neurosky headsets (Mind-Wave and MindWave Mobile), EPOC and FLEX from Emotiv, Ultracortex (Open BCI), ENOBIO (Neuroelectrics), DSI (QUASAR), interaXon, Smarting (mBrainTrain) or Quick (Cognionics) [ 110 , 112 , 113 , 118 ]. Especially Emotiv EPOC is a very popular and inexpensive solution which, despite the appearance of newer versions (Flex and Insight), is still being applied in numerous BCI-related projects [ 107 , 110 , 112 , 119 ]. MindWave Mobile and Yband also stand over other numerous inexpensive headsets, mainly due to their price and reliability.…”

Section: Overview Of Already Existing Methodsmentioning

confidence: 99%

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

Šidiková

Martínek

Kawala-Sterniuk

et al. 2020

Sensors

View full text Add to dashboard Cite

This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.

show abstract

“…Cumulative number of publications referring to BCI indexed by IEEE Xplore, Web of Science, PubMed and Scopus, and it is obvious the research on BCI is increasing year by year. [11]- [14], as shown in the Table. I. Four typical paradigms in the EEG signal, namely P300 event related potential (ERP), slow cortical potential (SCP), sensorimotor rhythms (SMR), and steady-state visual evoked potential (SSVEP) are used to analyse brain activities [15], and Fig.…”

Section: Introductionmentioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

View full text Add to dashboard Cite

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signalto-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.

show abstract

Consumer Grade EEG Measuring Sensors as Research Tools: A Review

Cited by 186 publications

References 205 publications

Assessing the Quality of Wearable EEG Systems Using Functional Connectivity

Assessing the Quality of Wearable EEG Systems Using Functional Connectivity

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

Contact Info

Product

Resources

About