MENEADY: Mental Readiness Alert System using BCI

Abstract

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.