Physiological Signals Based Mental Fatigue Analysis & Recognition: MEFAR
Description
The dataset was created specifically for analyzing mental fatigue based on physiological signals. It includes the following signals: EEG (Electroencephalography): Measures the electrical activity of the brain. BVP (Blood Volume Pulse): Captures changes in blood volume in peripheral blood vessels. EDA (Electrodermal Activity): Tracks changes in the electrical conductance of the skin. HR (Heart Rate): Records the number of heartbeats per minute. TEMP (Temperature): Monitors the body temperature of the participants. ACC (Acceleration): Measures the acceleration experienced by the participants. The measurements were collected from 23 participants in both the morning and evening sessions. To evaluate the participants' mental fatigue levels, the Chalder Fatigue Scale was utilized. This scale assigns a score to each participant based on their responses, with a score of 12 or higher indicating a positive mental fatigue condition. The dataset includes both raw and processed data. Raw data refers to the original recorded signals, while processed data typically involves signal preprocessing techniques such as filtering, artifact removal, and feature extraction.The processed data includes processed data based on different sampling frequencies (1 Hz, 32 Hz, and 64 Hz). These operations include downsampling, midsampling, and upsampling. The processed datasets are named MEFAR_DOWN, MEFAR_MID, and MEFAR_UP, corresponding to the processed data with downsampling, midsampling, and upsampling, respectively.This way, it was possible to analyze and compare data with different sampling frequencies. The dataset has been used for training deep learning and transfer learning models, which suggests that it may be suitable for developing machine learning algorithms for mental fatigue detection based on physiological signals. Additionally, demographic information about the participants is available in an Excel file called "general_info." It is important to ensure that the participants' anonymity and privacy are maintained in accordance with ethical guidelines. By sharing this dataset, researchers interested in mental fatigue analysis can utilize it for further investigations, algorithm development, and validation.