Electroencephalogram signal recording and processing for subjects’ engagement analysis with visual content

Since attention affects cognitive performance, it is essential to identify and keep track of students' attention during learning. The ability of interactive learning systems to modify tutoring content, provide efficient help strategies, and enhance learning outcomes may thus be made possible by getting a detailed understanding of a learner's mental state. In computer-based learning environments, keeping track of students' mental states is vital. This research is an investigation into the viability of using active learning to enhance student engagement index when exposed to varied visual stimuli. The study involves gathering EEG data from twenty participants (ten men and ten women) while they rest and interact with various virtual learning tools. The Allengers Neuro PLOT, a 28-channel wet electrode system, was used to collect the EEG data. EEG data under resting and audio-visual stimuli, both raw and pre-processed, are included in the work that follows. The research community will have access to the recorded data, which is supported by an advanced EEG data pre-processing pipeline. Note: The recorded EEG data uploaded here are referred to by the following abbreviations: Sub: Subject N: Non-Processed/Raw Data P: Processed Data BL: Baseline IT: Infotainment ET: Entertainment AL: Active Learning EC: Educational Content AP: Absolute Power RP: Relative Power Spect: Spectral Image Example: Sub1N_ET: Subject 1, Non-Processed, ET: Entertainment注意力会对认知表现产生影响，因此在学习过程中识别并追踪学生的注意力状态至关重要。而通过深入洞悉学习者的心理状态，交互式学习系统便可实现调整辅导内容、提供高效辅助策略以及优化学习成效的目标。在基于计算机的学习环境中，追踪学生的心理状态尤为关键。 本研究旨在探究在接触多样化视觉刺激时，采用主动学习（Active Learning）提升学生参与度指数的可行性。研究招募了20名受试者（10名男性、10名女性），在其静息状态以及与各类虚拟学习工具交互的过程中采集脑电图（Electroencephalogram, EEG）数据。本研究采用Allengers Neuro PLOT 28通道湿电极系统采集脑电图数据。后续公开的数据集涵盖了静息状态与视听刺激下的原始及预处理脑电图数据。本数据集依托先进的脑电图数据预处理流程，将向全球科研社区开放所有采集得到的数据。 备注：此处上传的脑电图数据采用以下缩写标识： Sub：受试者（Subject） N：未处理/原始数据（Non-Processed/Raw Data） P：预处理数据（Processed Data） BL：基线状态（Baseline） IT：信息娱乐（Infotainment） ET：娱乐（Entertainment） AL：主动学习（Active Learning） EC：教学内容（Educational Content） AP：绝对功率（Absolute Power） RP：相对功率（Relative Power） Spect：频谱图像（Spectral Image） 示例：Sub1N_ET：受试者1，原始未处理数据，ET代表娱乐场景。