Real-time classification of EEG signals using Machine Learning deployment

TL;DR

This paper presents a machine learning system that analyzes EEG signals in real-time to assess student concentration and comprehension during lectures, aiming to improve teaching methods and student engagement.

Contribution

It introduces a novel real-time EEG-based machine learning framework with a browser interface for monitoring student attention and understanding during lessons.

Findings

Effective real-time prediction of student concentration levels.

Implementation of a browser-based interface for educational monitoring.

Addressed challenges of deploying machine learning in live classroom settings.

Abstract

The prevailing educational methods predominantly rely on traditional classroom instruction or online delivery, often limiting the teachers' ability to engage effectively with all the students simultaneously. A more intrinsic method of evaluating student attentiveness during lectures can enable the educators to tailor the course materials and their teaching styles in order to better meet the students' needs. The aim of this paper is to enhance teaching quality in real time, thereby fostering a higher student engagement in the classroom activities. By monitoring the students' electroencephalography (EEG) signals and employing machine learning algorithms, this study proposes a comprehensive solution for addressing this challenge. Machine learning has emerged as a powerful tool for simplifying the analysis of complex variables, enabling the effective assessment of the students' concentration levels based on specific parameters. However, the real-time impact of machine learning models necessitates a careful consideration as their deployment is concerned. This study proposes a machine learning-based approach for predicting the level of students' comprehension with regard to a certain topic. A browser interface was introduced that accesses the values of the system's parameters to determine a student's level of concentration on a chosen topic. The deployment of the proposed system made it necessary to address the real-time challenges faced by the students, consider the system's cost, and establish trust in its efficacy. This paper presents the efforts made for approaching this pertinent issue through the implementation of innovative technologies and provides a framework for addressing key considerations for future research directions.