Journal: Data in Brief

Article Title: Neurophysiological and biosignal data for investigating occupational mental fatigue: MEFAR dataset

doi: 10.1016/j.dib.2023.109896

Figure Lengend Snippet:

Article Snippet: Data collection , The physiological data utilized in this dataset was obtained through the utilization of two distinct devices and software applications. The NeuroExperimenter app was used to get the NeuroSky MindWave brain signals, while the E4 Realtime app was used to get the Empatica E4 biosignal data. During the EEG data collection process, the study participants were equipped with the NeuroSky MindWave headset, and the NeuroExperimenter application was utilized to effectively capture and record the EEG signals. The application facilitated the contemporaneous recording of electroencephalographic data by employing a single-channel electrode affixed to the cranial region. The EEG signals were recorded with a sampling rate of 1 Hz. During data collection, the Empatica E4 wristband was utilized to capture physiological signals including BVP, EDA, HR, skin temperature, and a 3-axis accelerometer. The Empatica-provided "Realtime" application was employed to capture the physiological signals stemming from the E4 device. The subjects donned an E4 wristband on their non-dominant wrist, which facilitated the uninterrupted recording of their physiological indicators throughout their professional engagements. The BVP was recorded using a sampling frequency of 64 Hz to effectively capture any variations in the signal. EDA was recorded by sampling the signal at a frequency of 4 Hz. Human HR and body temperature were obtained as instantaneous measurements at regularly spaced intervals, while the accelerometer was represented by the ACC signal and sampled at a frequency of 32 Hz to capture and analyze movement and activity levels. Thorough configuration and adjustment procedures were executed on every apparatus and software program to guarantee the precise acquisition of data. Physiological signals recorded in separate files were combined according to different sample rates using resampling techniques, and sub-datasets of three different sizes were created. The questionnaire data includes demographic information such as age, occupation, education, employment status, etc. of the participants and mental fatigue scores calculated according to their responses to the Chalder Fatigue Scale. In the Chalder Fatigue Scale, the responses of the participants were made according to a 4-point Likert scale in the range of 0–3. The participant's responses to the questions on the scale were scored as 0 if less than usual, 1 if as usual, 2 if more than usual, and 3 if much more than usual..

Techniques: Recognition Signal, Software, Sampling, Activity Assay

Journal: Data in Brief

Article Title: Neurophysiological and biosignal data for investigating occupational mental fatigue: MEFAR dataset

doi: 10.1016/j.dib.2023.109896

Figure Lengend Snippet: NeuroSky Mindwave EEG data description.

Article Snippet: Data collection , The physiological data utilized in this dataset was obtained through the utilization of two distinct devices and software applications. The NeuroExperimenter app was used to get the NeuroSky MindWave brain signals, while the E4 Realtime app was used to get the Empatica E4 biosignal data. During the EEG data collection process, the study participants were equipped with the NeuroSky MindWave headset, and the NeuroExperimenter application was utilized to effectively capture and record the EEG signals. The application facilitated the contemporaneous recording of electroencephalographic data by employing a single-channel electrode affixed to the cranial region. The EEG signals were recorded with a sampling rate of 1 Hz. During data collection, the Empatica E4 wristband was utilized to capture physiological signals including BVP, EDA, HR, skin temperature, and a 3-axis accelerometer. The Empatica-provided "Realtime" application was employed to capture the physiological signals stemming from the E4 device. The subjects donned an E4 wristband on their non-dominant wrist, which facilitated the uninterrupted recording of their physiological indicators throughout their professional engagements. The BVP was recorded using a sampling frequency of 64 Hz to effectively capture any variations in the signal. EDA was recorded by sampling the signal at a frequency of 4 Hz. Human HR and body temperature were obtained as instantaneous measurements at regularly spaced intervals, while the accelerometer was represented by the ACC signal and sampled at a frequency of 32 Hz to capture and analyze movement and activity levels. Thorough configuration and adjustment procedures were executed on every apparatus and software program to guarantee the precise acquisition of data. Physiological signals recorded in separate files were combined according to different sample rates using resampling techniques, and sub-datasets of three different sizes were created. The questionnaire data includes demographic information such as age, occupation, education, employment status, etc. of the participants and mental fatigue scores calculated according to their responses to the Chalder Fatigue Scale. In the Chalder Fatigue Scale, the responses of the participants were made according to a 4-point Likert scale in the range of 0–3. The participant's responses to the questions on the scale were scored as 0 if less than usual, 1 if as usual, 2 if more than usual, and 3 if much more than usual..

Techniques: Concentration Assay, Expressing

Journal: Data in Brief

Article Title: Neurophysiological and biosignal data for investigating occupational mental fatigue: MEFAR dataset

doi: 10.1016/j.dib.2023.109896

Figure Lengend Snippet:

Article Snippet: Data collection , The physiological data utilized in this dataset was obtained through the utilization of two distinct devices and software applications. The NeuroExperimenter app was used to get the NeuroSky MindWave brain signals, while the E4 Realtime app was used to get the Empatica E4 biosignal data. During the EEG data collection process, the study participants were equipped with the NeuroSky MindWave headset, and the NeuroExperimenter application was utilized to effectively capture and record the EEG signals. The application facilitated the contemporaneous recording of electroencephalographic data by employing a single-channel electrode affixed to the cranial region. The EEG signals were recorded with a sampling rate of 1 Hz. During data collection, the Empatica E4 wristband was utilized to capture physiological signals including BVP, EDA, HR, skin temperature, and a 3-axis accelerometer. The Empatica-provided "Realtime" application was employed to capture the physiological signals stemming from the E4 device. The subjects donned an E4 wristband on their non-dominant wrist, which facilitated the uninterrupted recording of their physiological indicators throughout their professional engagements. The BVP was recorded using a sampling frequency of 64 Hz to effectively capture any variations in the signal. EDA was recorded by sampling the signal at a frequency of 4 Hz. Human HR and body temperature were obtained as instantaneous measurements at regularly spaced intervals, while the accelerometer was represented by the ACC signal and sampled at a frequency of 32 Hz to capture and analyze movement and activity levels. Thorough configuration and adjustment procedures were executed on every apparatus and software program to guarantee the precise acquisition of data. Physiological signals recorded in separate files were combined according to different sample rates using resampling techniques, and sub-datasets of three different sizes were created. The questionnaire data includes demographic information such as age, occupation, education, employment status, etc. of the participants and mental fatigue scores calculated according to their responses to the Chalder Fatigue Scale. In the Chalder Fatigue Scale, the responses of the participants were made according to a 4-point Likert scale in the range of 0–3. The participant's responses to the questions on the scale were scored as 0 if less than usual, 1 if as usual, 2 if more than usual, and 3 if much more than usual..

Techniques: Recognition Signal, Software, Sampling, Activity Assay

Journal: Data in Brief

Article Title: WheelSimPhysio-2023 dataset: Physiological and questionnaire-based dataset of immersive multisensory wheelchair simulator from 58 participants

doi: 10.1016/j.dib.2024.110535

Figure Lengend Snippet:

Article Snippet: , - Hardware/Software specifications: ○ Wheelchair Simulator : the application was developed using the Unity game engine in PC machine that operates with monitor or VR HMD. ○ Physiological data : the physiological response was collected by using the Empatica E4 wristband. It has four sensors to determine the blood volume pressure (BVP) at a sample rate of 64 Hz, interbeat interval (IBI), Heart Rate (HR) in a sample rate of 1 Hz, electrodermal activity (GSR/EDA) at a sample rate of 4 Hz, XYZ raw acceleration at a sample rate of 32 Hz and the skin temperature at a sample rate of 4 Hz. The EEG data was collected using Mindwave Mobile from Neurosky, is one channel EEG device, the electrodes are dry type, its bandwidth is between 3 and 100 Hz (Hz), with 12 bits of resolution, sample rate is 512 Hz and the transmission is made via Bluetooth. Eye-gaze and head pose were collected using a HP w200 with OpenFace framework. ○ Supplementary data: These data consist of templates used for the consent form and questionnaires in .pdf format; the response letters from the institutional ethics committee; and the summary answers (anonymized) from the participants in .xlsx format.".

Techniques: Activity Assay, Transmission Assay