An attention‐driven videogame based on <scp>steady‐state</scp> motion visual evoked potentials

Pérez-Valero, Eduardo; López-Gordo, M. A.; Vaquero-Blasco, Miguel A.

doi:10.1111/exsy.12682

Cited by 6 publications

(7 citation statements)

References 30 publications

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“…The radial contraction-expansion motion paradigm has been developed for spellers at the application level, and it works well. In addition, this paradigm has also been used to develop attention-driven games, and researchers believe that this paradigm can be used to train participants’ attention, which can be used in special education schools or centers dealing with attention disorders in the future [29].…”

Section: Resultsmentioning

confidence: 99%

An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady-state visual evoked paradigm

Zhao

Wang

et al. 2022

Brain Science Advances

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The radial contraction-expansion motion paradigm is a novel steady-state visual evoked experimental paradigm, and the electroencephalography (EEG) evoked potential is different from the traditional luminance modulation paradigm. The signal energy is concentrated chiefly in the fundamental frequency, while the higher harmonic power is lower. Therefore, the conventional steady-state visual evoked potential recognition algorithms optimizing multiple harmonic response components, such as the extended canonical correlation analysis (eCCA) and task-related component analysis (TRCA) algorithm, have poor recognition performance under the radial contraction-expansion motion paradigm. This paper proposes an extended binary subband canonical correlation analysis (eBSCCA) algorithm for the radial contraction-expansion motion paradigm. For the radial contraction-expansion motion paradigm, binary subband filtering was used to optimize the weighting coefficients of different frequency response signals, thereby improving the recognition performance of EEG signals. The results of offline experiments involving 13 subjects showed that the eBSCCA algorithm exhibits a better performance than the eCCA and TRCA algorithms under the stimulation of the radial contraction-expansion motion paradigm. In the online experiment, the average recognition accuracy of 13 subjects was 88.68% ± 6.33%, and the average information transmission rate (ITR) was 158.77 ± 43.67 bits/min, which proved that the algorithm had good recognition effect signals evoked by the radial contraction-expansion motion paradigm.

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Section: Resultsmentioning

confidence: 99%

An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady-state visual evoked paradigm

Zhao

Wang

et al. 2022

Brain Science Advances

View full text Add to dashboard Cite

show abstract

“…The fast Fourier transform (FFT) was applied to the raw signal to compute the power spectral density (PSD) of each brainwave [31]. Specifically, we extract the PSD of beta brainwave (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), which is related to the state of concentration, attention, or alertness [32,33].…”

Section: Methodsmentioning

confidence: 99%

“…The miniaturization of electronics and the decrease in the price of the devices in the last decades have enabled the development and evolution of low-cost portable EEG (PEEG) devices, allowing their use not only in clinical diagnosis areas but as a tool for research, education, entertainment, or engineering. PEEGs are widely used for brain-computer interface (BCI) systems [15], allowing the human being to use mental states to control devices, like robots [16] or machines [17], as well as measure attention [18] or play videogames [19,20], thanks to brainwaves recorded with the EEG. The PEEG device used in this study is the Neurosky Mindwave (NeuroSky Inc., San Jose, CA, USA), which has been validated for scientific use in assessing variations in the cognitive state [21].…”

mentioning

confidence: 99%

Low-Cost EEG Multi-Subject Recording Platform for the Assessment of Students’ Attention and the Estimation of Academic Performance in Secondary School

Fuentes-Martinez,

Romero,

Lopez-Gordo

et al. 2023

Sensors

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The level of student attention in class greatly affects their academic performance. Teachers typically rely on visual inspection to react to students’ attention in time, but this subjective method leads to inconsistencies across classes. Online education exacerbates the issue as students can turn off cameras and microphones to keep their own privacy. To address this, we present a novel, low-cost EEG-based platform for assessing students’ attention and estimating their academic performance. In a study involving 34 secondary school students (aged 14 to 16), participants watched an academic video and answered evaluation questions while their EEG activity was recorded using a commercial headset. The results demonstrate a significant correlation (0.53, p-value = 0.003) between the power spectral density (PSD) of the EEG beta band (12–30 Hz) and students’ academic performance. Additionally, there was a notable difference in PSD-beta between high and low academic performers. These findings encourage the use of PSD-beta for the immediate and objective assessment of both the student attention and the subsequent academic performance. The platform offers valuable and objective feedback to teachers, enhancing the effectiveness of both face-to-face and online teaching and learning environments.

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“…The BCI is a technology that collects and analyses neural (brain) signals in order to establish a direct high-bandwidth communication link between the brain and the computer [2]. Education, production, marketing, security, and entertainment are just a few of the fields where brain signal reading and remote communication have made an influence [3]- [5]. Figure 1 illustrates the comparison between various non-invasive and invasive brain computer interface (BCI) technology that is presently available [6], [7] where in the latter, an IC is surgically placed inside the brain, which is why non-invasive BCI, which just requires the use of a headset or a cap with active electrode system, is preferred [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Electrical Appliance Switching Controller by Brain Wave Spectrum Evaluation Using a Wireless EEG Headset

F.¹,

Miskon²,

Hamzah³

et al. 2021

IJETAE

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Disabled people are usually unable to interact with their surroundings efficiently, and performing tasks like switching an appliance on or off can be troublesome if the user is bedridden, for example. This article discusses an electrical appliance switching controller using a wireless EEG headset that is aimed to aid elderly people and the disabled. The system comprises of a MindLink EEG headset that is Bluetooth-connected to an Arduino microcontroller board. The system permits the user to separately switch on and off the 4 electrical devices connected to the power socket. The EEG signal is obtained to investigate the brain activity throughout the experiments done. Based on the brain wave signals read, attention and meditation are determined to be the most suitable for this project and is used to trigger the relay switching of the power socket. It is found that the response time to trigger the switching is slow as some users require practice or training to control their brain wave signals effectively. The work performed provides a rudimentary insight of a BCI system functionalities and presents a brainwave-controlled hardware switching for the bedridden or disabled patients.

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An attention‐driven videogame based on steady‐state motion visual evoked potentials

Cited by 6 publications

References 30 publications

An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady-state visual evoked paradigm

An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady-state visual evoked paradigm

Low-Cost EEG Multi-Subject Recording Platform for the Assessment of Students’ Attention and the Estimation of Academic Performance in Secondary School

Electrical Appliance Switching Controller by Brain Wave Spectrum Evaluation Using a Wireless EEG Headset

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