MEFAR Dataset: Neurophysiological and Biosignal Data

Published: 24 October 2023| Version 5 | DOI: 10.17632/z3g26tphnv.5
SEYMA DERDIYOK, Fatma Patlar Akbulut


The dataset was carefully generated with the purpose of investigating mental fatigue through the analysis of physiological signals. The signals that are included are as follows: Electroencephalography (EEG) is a technique utilized to assess the electrical activity of the brain. The blood volume pulse (BVP) is a measurement technique that records fluctuations in blood volume inside the peripheral blood vessels. Electrodermal activity (EDA) is a physiological measure that monitors variations in the electrical conductivity of the skin. The heart rate (HR) is data that measures the frequency of heartbeats within a given timeframe, typically expressed as the number of beats per minute. The temperature sensor is responsible for monitoring the body temperature of the participants. The 3-axis accelerometer (ACC) data quantifies the rate of change in velocity experienced by the individuals involved. Data was gathered from a sample of 23 individuals during both morning and evening sessions. The Chalder Fatigue Scale was employed in order to assess the levels of mental weariness among the participants. The scale utilized in this study offers a numerical score to each participant, which is determined based on their responses. A score of 12 or above on this scale is indicative of a positive mental fatigue condition. The dataset includes both unprocessed/raw and processed data. The term "raw data" pertains to unaltered recorded signals, whereas "processed data" generally includes signal preprocessing methods such as filtering, artifact removal, and feature extraction. The processed data comprises data that has been processed using various sampling frequencies, namely 1 Hz, 32 Hz, and 64 Hz. The operations encompass downsampling, midsampling, and upsampling. The datasets that have undergone processing are denoted as MEFAR_DOWN, MEFAR_MID, and MEFAR_UP, representing the treated data with downsampling, midsampling, and upsampling, respectively. Furthermore, comprehensive demographic data pertaining to the participants can be found in an Excel spreadsheet named "general_info." Maintaining the identity and privacy of participants is crucial in order to adhere to ethical requirements. This dataset can be shared among researchers that are interested in analyzing mental exhaustion, enabling them to conduct more investigations, develop algorithms, and validate their findings.



Mental State, Physiological Signal Processing, Emotion Representation