Attention Patterns Detection using Brain Computer Interfaces

TL;DR

This paper presents a method using brain computer interfaces and recurrent neural networks to detect and analyze attention patterns from EEG data, aiming to enhance understanding of human attention in learning contexts.

Contribution

It introduces a novel approach combining BCI and RNNs to assess attention levels and their impact on learning, advancing biometric-based human-computer interaction.

Findings

Effective detection of attention states from EEG data

Demonstrated potential for real-time attention monitoring

Improved understanding of attention dynamics in learning

Abstract

The human brain provides a range of functions such as expressing emotions, controlling the rate of breathing, etc., and its study has attracted the interest of scientists for many years. As machine learning models become more sophisticated, and bio-metric data becomes more readily available through new non-invasive technologies, it becomes increasingly possible to gain access to interesting biometric data that could revolutionize Human-Computer Interaction. In this research, we propose a method to assess and quantify human attention levels and their effects on learning. In our study, we employ a brain computer interface (BCI) capable of detecting brain wave activity and displaying the corresponding electroencephalograms (EEG). We train recurrent neural networks (RNNS) to identify the type of activity an individual is performing.