A Multi-Label EEG Dataset for Mental Attention State Classification in Online Learning

TL;DR

This paper introduces a new multi-label EEG dataset for classifying mental attention states in online learning, addressing data scarcity and standardization issues, and providing a valuable resource for future research.

Contribution

The authors created and validated a standardized, multi-label EEG dataset with attention and psychological state annotations for online learning environments.

Findings

Collected 1,060 minutes of EEG data from 20 subjects

Validated dataset quality through extensive analysis

Provided insights into attention and psychological state relationships

Abstract

Attention is a vital cognitive process in the learning and memory environment, particularly in the context of online learning. Traditional methods for classifying attention states of online learners based on behavioral signals are prone to distortion, leading to increased interest in using electroencephalography (EEG) signals for authentic and accurate assessment. However, the field of attention state classification based on EEG signals in online learning faces challenges, including the scarcity of publicly available datasets, the lack of standardized data collection paradigms, and the requirement to consider the interplay between attention and other psychological states. In light of this, we present the Multi-label EEG dataset for classifying Mental Attention states (MEMA) in online learning. We meticulously designed a reliable and standard experimental paradigm with three attention states: neutral, relaxing, and concentrating, considering human physiological and psychological characteristics. This paradigm collected EEG signals from 20 subjects, each participating in 12 trials, resulting in 1,060 minutes of data. Emotional state labels, basic personal information, and personality traits were also collected to investigate the relationship between attention and other psychological states. Extensive quantitative and qualitative analysis, including a multi-label correlation study, validated the quality of the EEG attention data. The MEMA dataset and analysis provide valuable insights for advancing research on attention in online learning. The dataset is publicly available at \url{https://github.com/GuanjianLiu/MEMA}.