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

Abstract

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.

Applications of Brain Wave Classification for Controlling an Intelligent Wheelchair

“…The average accuracy of other authors using the EPOC and the magnitude of frequency components or the power spectral density (square of the magnitude) as features was 74-100% for Abiyev et al [57], 70% for Hurtado-Rincon et al [59], and 86-92% for Lin and Lo [60], and Siribunyaphat and Punsawad [67]. More recent works [49,68,69] have also achieved important F1-scores, using different EEG headsets. This reflects a promising area to explore for controlling intelligent wheelchairs.…”

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