EEG Dataset for Nonlinear Dynamics-Based Characterization of Cognitive States in Engineering Students

This dataset contains electroencephalographic (EEG) recordings acquired from engineering students using Muse 2 wearable dry-electrode headsets. The dataset was collected to support the nonlinear dynamics-based characterization and machine learning-based classification of cognitive states associated with different types of information processing during educational activities. The recordings include EEG signals from four channels: TP9, AF7, AF8, and TP10, sampled at 256 Hz. The dataset includes recordings from control and experimental groups. The experimental condition involved ASMR-based audiovisual stimulation. Temporal markers indicate the experimental segments and cognitive tasks, during which students watched videos containing relevant, irrelevant, and false information. The data can be used for studies involving EEG signal processing, nonlinear and chaotic descriptors, cognitive-state classification, educational neuroscience, and human–machine interaction. The dataset is accompanied by documentation describing the file structure, EEG channels, sampling frequency, temporal markers, and experimental groups. Personal identifiers were removed or anonymized before publication. The H/M (Hombre/Mujer) sex code was retained as an anonymized demographic variable because it is relevant for group-level EEG analyses.