Abstract
This dataset contains a pilot multimodal physiological recording collected using a four-channel wearable EEG device (Muse S). The dataset comprises 17,155 timestamped observations recorded over approximately 67 seconds, with an effective sampling rate of approximately 256 Hz. This data was collected at the Psychology Lab of the University of Greater Manchester, United Kingdom.
The dataset includes EEG-derived spectral features across the delta, theta, alpha, beta, and gamma bands at four scalp locations (TP9, AF7, AF8, and TP10), together with raw EEG signals. To support multimodal analysis, the dataset also contains three-axis accelerometer and gyroscope measurements, enabling the examination of head movement alongside neural activity. Additional metadata include headband/contact quality indicators, battery level, and event/marker information.
The dataset is intended to support research into cognitive workload, mental-state monitoring, multimodal EEG analysis, wearable sensing, and machine-learning-based classification, particularly approaches that combine EEG spectral information with movement-related features.