The Emotiv EPOC interface paradigm in Human-Computer Interaction

Ancău, Dorina; Roman, N. M.; Ancău, Mircea

doi:10.1051/matecconf/201713704001

Cited by 3 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the EEG signal acquisition system called Emotiv EPOC Flex Saline [13] is used to monitor and collect the subjects' EEG signals. e corresponding relationship between the electrode symbol and the brain scalp area is shown in Table 1.…”

Section: Eeg Signal Collectingmentioning

confidence: 99%

An Online Teaching Video Evaluation Scheme Based on EEG Signals and Machine Learning

Huang

Han

Xiao

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Nowadays, the number of online teaching videos is rising rapidly; how to evaluate the actual effect of these videos objectively and justly is a hot issue. To solve this problem, this paper proposes a video learning effect evaluation scheme based on EEG signals and machine learning, and the k-nearest neighbor regression algorithm is adopted to complete the mental workload test because the determination coefficient of the training set can achieve 1.0 and no other model can achieve this value. Furthermore, the random forest algorithm is employed to complete the concentration test, and the determination coefficient of the training set is 0.978 and that of the test set is 0.929, both better than the existing relevant online learning video evaluation models. Finally, the effect of teaching videos is evaluated based on the learning efficiency of subjects. Through the student satisfaction test, it is found that this scheme can indeed improve students’ satisfaction with watching teaching video, and the increase rate can achieve 85%. This scheme could not only promote teachers to continuously improve their teaching level, but also provide a more reasonable reference for students to choose teaching videos.

show abstract

Section: Eeg Signal Collectingmentioning

confidence: 99%

An Online Teaching Video Evaluation Scheme Based on EEG Signals and Machine Learning

Huang

Han

Xiao

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

10 years of EPOC: A scoping review of Emotiv’s portable EEG device

Williams

McArthur

Badcock

2020

Preprint

View full text Add to dashboard Cite

BACKGROUND Commercially-made low-cost electroencephalography (EEG) devices have become increasingly available over the last decade. One of these devices, Emotiv EPOC, is currently used in a wide variety of settings, including brain-computer interface (BCI) and cognitive neuroscience research. PURPOSE The aim of this study was to chart peer-reviewed reports of Emotiv EPOC projects to provide an informed summary on the use of this device for scientific purposes. 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: PsychINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, clinical, signal processing, experimental research, and validation) and location of use (as indexed by the first author’s address). RESULTS We identified 382 relevant studies. The top five publishing countries were the United States (n = 35), India (n = 25), China (n = 20), Poland (n = 17), and Pakistan (n = 17). The top five publishing cities were Islamabad (n = 11), Singapore (n = 10), Cairo, Sydney, and Bandung (n = 7 each). Most of these studies used Emotiv EPOC for BCI purposes (n = 277), followed by experimental research (n = 51). Thirty-one studies were aimed at validating EPOC as an EEG device and a handful of studies used EPOC for improving EEG signal processing (n = 12) or for clinical purposes (n = 11). CONCLUSIONS In its first 10 years, Emotiv EPOC has been used around the world in diverse applications, from control of robotic limbs and wheelchairs to user authentication in security systems to identification of emotional states. Given the widespread use and breadth of applications, it is clear that researchers are embracing this technology.

show abstract