Application of Portable EEG Device in Detection and Classification Drowsiness by Support Vector Machine

Pham, Thi Tram Anh; Nguyen, Thi Diem Hang; Le, Quoc Khai; Linh, Huynh Quang

doi:10.1007/978-981-13-5859-3_90

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Pham et al ( 2018 ) directly examined real-time drowsiness detection using an Emotiv Epoc. The primary features extracted were spectral features, and classification was performed with SVM.…”

Section: Resultsmentioning

confidence: 99%

“…Instead of detecting drowsiness, the system was used to increase focus, and the authors reported an increase of 10% in healthy subjects that used the EEGbased controls. Pham et al (2018) directly examined real-time drowsiness detection using an Emotiv Epoc. The primary features extracted were spectral features, and classification was performed with SVM.…”

Section: Openbci Ultracortexmentioning

confidence: 99%

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A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

LaRocco

Paeng

2020

Front. Neuroinform.

116

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Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. Of 46 relevant studies, ∼27 reported an accuracy score. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. The second lowest accuracy reported was 79.4% with an OpenBCI study. In many cases, algorithmic optimization remains necessary. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Each specific device has its own capabilities, tradeoffs, and limitations. Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices; however, reliability issues must still be addressed in an occupational context.

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Section: Resultsmentioning

confidence: 99%

Section: Openbci Ultracortexmentioning

confidence: 99%

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

LaRocco

Paeng

2020

Front. Neuroinform.

116

View full text Add to dashboard Cite

show abstract

“…For instance, the Emotiv was used in studies examining pilots' reactions to unexpected events [78], reaction time [79], and mental fatigue and alertness [80]. Similarly, many experimental studies aimed to detect drowsy states [16,[81][82][83][84][85]. Such paradigms have been adopted to improve road safety with the creation of early warning systems that alert drivers to their own fatigue before an accident can occur [86][87][88][89].…”

Section: Plos Onementioning

confidence: 99%

A scoping review on the use of consumer-grade EEG devices for research

Sabio,

Williams,

McArthur

et al. 2024

PLoS ONE

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Background Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience. Purpose The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG. Methods We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author’s country. Results We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes. Conclusions Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices.

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“…experimental studies aimed to detect drowsy states [16,[75][76][77][78][79]. Such paradigms have been adopted to improve road safety with the creation of early warning systems that alert a driver before an accident can occur [80][81][82][83] artefacts.…”

Section: Experimental Researchmentioning

confidence: 99%

“…For instance, the Emotiv was used in studies examining pilots' reactions to unexpected events [72], reaction time [73], and mental fatigue and alertness [74]. Similarly, many experimental studies aimed to detect drowsy states [16,[75][76][77][78][79]. Such paradigms have been adopted to improve road safety with the creation of early warning systems that alert a driver before an accident can occur [80][81][82][83] artefacts.…”

Section: Experimental Researchmentioning

confidence: 99%

A scoping review on the use of consumer-grade EEG devices for research

Sabio

Williams

McArthur

et al. 2022

Preprint

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BACKGROUND: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience. PURPOSE: The aim of this study was to chart peer-review articles that used currently available consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG. METHODS: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following electronic databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country. RESULTS: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were most used, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common use was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes. CONCLUSIONS: Consumer-grade EEG has proven to be a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who wish to use these devices.

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Application of Portable EEG Device in Detection and Classification Drowsiness by Support Vector Machine

Cited by 5 publications

References 3 publications

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

A scoping review on the use of consumer-grade EEG devices for research

A scoping review on the use of consumer-grade EEG devices for research

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