Correlation of Attention and Relaxation Levels to Interactive Self-learning Classroom Indexed by eSense Meters

Srimaharaj, Wanus; Chaipakornwong, Tinnaphol; Punyahotra, Vichit; Chaising, Supansa; Temdee, Punarumol; Chaisricharoen, Roungsan; Sittiprapaporn, Phakkharawat

doi:10.1007/978-3-030-63128-4_62

Cited by 1 publication

(1 citation statement)

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“…If the participants have less attention, then it will be difficult to understand instructions, socialize with others, and have bad memory due to drained mental energy upon mental activity (Dinesh Anton Raja et al, 2020). It also demonstrates the measurement of mental focus and concentration either within or outside the control of mental activity through the attention eSense meter in Neurosky (Srimaharaj et al, 2021). The attention meter level may be declined because of distractions, wandering minds, less focus, or fear.…”

Section: Signal Processingmentioning

confidence: 99%

MENEADY: Mental Readiness Alert System using BCI

Hazirah,

Lahak,

Hossain

et al. 2022

MyJICT

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Digital platforms have been utilized in teaching and learning sessions especially during the Covid-19 pandemic. However, sessions that are too long can reduce concentration due to lack of mental readiness. Because of this, the efficiency of the learning session may be impacted. Although electroencephalogram (EEG) signals may provide the overview of various mental states, no online application has been developed to detect mental readiness when students are learning online. Therefore, this paper aims to describe the design and development of MENEADY (MENtal rEADiness alert system), a EEG-based BCI that detects mental readiness and provides alert to users using the NeuroSky MindWave Mobile 2 headset. MENEADY is developed using Python. The mental readiness states are measured using the Neurosky attention eSense meter algorithm. The back-end is handled by the Flask framework, while the front-end is managed by Bootstrap. The design of MENEADY can also be adapted for an automated neurofeedback performing intervention neural activation states.

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Section: Signal Processingmentioning

confidence: 99%