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AI Driven Cognitive Level Analysis of Students Using EEG Data

Year: 2025

Type: article

Abstract: This paper introduces a web-based EEG analysis system for assessing student cognitive performance using AI techniques like CNN, LSTM, and Transformers to evaluate attention, concentration, cognitive load, and relaxation. Developed with Flask and SQLite, it features admin/student dashboards, real-time monitoring, and automated PDF reports. The system extracts frequency-domain features from EEG signals for educational insights, achieving high accuracy in cognitive state classification.

Source: International Journal of Engineering Development and Research

DOI: 10.56975/ijedr.v13i4.302048

Authors: Omkar R Takare, Akshata Dunagi

Institution: Rani Channamma University, Belagavi

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Quality education

Open Access status: hybrid

OpenAlex ID: W7094960511