Cognitive Fatigue

2. Cognitive Fatigue

While executing the proposed tasks, the participants’ physiological signals were monitored using two biosignalsplux devices from PLUX Wireless Biosignals, Lisbon, Portugal, with a sampling frequency of 100 Hz a resolution of 16 bits (24 bits in the case of fNIRS). Six different sensors were used: EEG and fNIRS positioned around the F7 and F8 of the 10–20 system (dorsolateral prefrontal cortex is often used to assess CW and fatigue as well as cognitive states); ECG monitored an approximation of Lead I of the Einthoven system, EDA placed on the palm of the non-dominant hand; ACC was positioned on the right side of the head to measure head movement and overall posture changes, and the RIP sensor was attached to the upper-abdominal area to measure the respiration cycles—the combination of the three allows to infer about the response of the Autonomic Nervous System (ANS) of the human body, namely, the response of the sympathetic and parasympathetic nervous system.
2.1. Experimental design
Cognitive fatigue (CF) is a phenomenon that arises following the prolonged engagement in mentally demanding cognitive tasks. Thus, we developed an experimental procedure that involved three demanding tasks: a digital lesson in Jupyter Notebook format, three repetitions of Corsi-Block task, and two repetitions of a concentration test.
Before the Corsi-Block task and after the concentration task there were periods of baseline of two min. In our analysis, the first baseline period, although not explicitly present in the dataset, was designated as representing no CF, whereas the final baseline period was designated as representing the presence of CF. Between repetitions of the Corsi-Block task, there were periods of baseline of 15 s after the task and of 30 s before the beginning of each repetition of the task.
2.2. Data recording
A data sample of 10 volunteer participants (4 females) aged between 22 and 48 years old (M = 28.2, SD = 7.6) took part in this study. All volunteers were recruited at NOVA School of Science and Technology, fluent in English, right-handed, none reported suffering from psychological disorders, and none reported taking regular medication. Written informed consent was obtained before participating and all Ethical Procedures approved by the Ethics Committee of NOVA University of Lisbon were thoroughly followed.In this study, we omitted the data from one participant due to the insufficient duration of data acquisition.
2.3. Data labelling
The labels easy, difficult, very difficult and repeat found in the ECG_lesson_answers.txt files represent the subjects' opinion of the content read in the ECG lesson. The repeat label represents the most difficult level. It's called repeat because when you press it, the answer to the question is shown again. This system is based on the Anki system, which has been proposed and used to memorise information effectively. In addition, the PB description JSON files include timestamps indicating the start and end of cognitive tasks, baseline periods, and other events, which are useful for defining CF states as we defined in 2.1.
2.4. Data description

Biosignals include EEG, fNIRS (not converted to oxi and deoxiHb), ECG, EDA, respiration (RIP), accelerometer (ACC), and push-button data (PB). All signals have already been converted to physical units. In each biosignal file, the first column corresponds to the timestamps.

HCI features encompass keyboard, mouse, and screenshot data. Below is a Python code snippet for extracting screenshot files from the screenshots CSV file.
import base64
from os import mkdir
from os.path import join

file = '...'

with open(file, 'r') as f:
    lines = f.readlines()

for line in lines[1:]:
    timestamp = line.split(',')[0]
    code = line.split(',')[-1][:-2]
    imgdata = base64.b64decode(code)
    filename = str(timestamp) + '.jpeg'

    mkdir('screenshot')

    with open(join('screenshot', filename), 'wb') as f:
        f.write(imgdata)
A characterization file containing age and gender information for all subjects in each dataset is provided within the respective dataset folder (e.g., D2_subject-info.csv). Other complementary files include (i) description of the pushbuttons to help segment the signals (e.g., D2_S2_PB_description.json) and (ii) labelling (e.g., D2_S2_ECG_lesson_results.txt). The files D2_Sx_results_corsi-block_board_1.json and D2_Sx_results_corsi-block_board_2.json show the results for the first and second iterations of the corsi-block task, where, for example, row_0_1 = 12 means that the subject got 12 pairs right in the first row of the first board, and row_0_2 = 12 means that the subject got 12 pairs right in the first row of the second board.