Data-driven natural computational psychophysiology in class

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Foilsithe in:Cognitive Neurodynamics vol. 18, no. 6 (Dec 2024), p. 3477
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Springer Nature B.V.
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022 |a 1871-4080 
022 |a 1871-4099 
024 7 |a 10.1007/s11571-024-10126-9  |2 doi 
035 |a 3146658221 
045 2 |b d20241201  |b d20241231 
245 1 |a Data-driven natural computational psychophysiology in class 
260 |b Springer Nature B.V.  |c Dec 2024 
513 |a Journal Article 
520 3 |a Objective. The assessment of mental fatigue (MF) and attention span in educational and healthcare settings frequently relies on subjective scales or methods such as induced-task interruption tools. However, these approaches are deficient in real-time evaluation and dynamic definitions. To address this gap, this paper proposes a Continuous Quantitative Scale (CQS) that allows for the natural and real-time measurement of MF based on group-synchronized electroencephalogram (EEG) data. Approach. In this study, computational psychophysiology was used to measure MF scores during a realistic class. Our methodology continuously monitored participants’ psychological states without interrupting their regular routines, providing an objective evaluation. By analyzing multi-subject brain-computer interface (mBCI) data with a collaborative computing approach, the group-synchronized data were obtained from 10 healthy participants to assess MF levels. Each participant wore an EEG headset for only 10 min of preparation before performing a sustained task for 80 min. Main results. Our findings indicate that a lecture duration of 18.9 min is most effective, while a duration of 43.1 min leads to heightened MF levels. By focusing on the group-level simultaneous data analysis, the effects of individual variability were mitigated and the efficiency of cognitive computing was improved. From the perspective of a neurocomputational measure, these results confirm previous research. Significance. The proposed CQS provides a reliable, objective, memory- and emotion-free approach to the assessment of MF and attention span. These findings have significant implications not only for education, but also for the study of group cognitive mechanisms and for improving the quality of mental healthcare. 
653 |a Physiology 
653 |a Attention task 
653 |a Brain 
653 |a Psychophysiology 
653 |a Fatigue 
653 |a Computation 
653 |a Attention 
653 |a Biochips 
653 |a Implants 
653 |a Electroencephalography 
653 |a Computer applications 
653 |a Time synchronization 
653 |a Physiological psychology 
653 |a Evaluation 
653 |a Cognition & reasoning 
653 |a Time measurement 
653 |a Health care 
653 |a Data analysis 
653 |a Human-computer interface 
653 |a Inverse problems 
653 |a EEG 
653 |a Experiments 
653 |a Design 
653 |a Real time 
773 0 |t Cognitive Neurodynamics  |g vol. 18, no. 6 (Dec 2024), p. 3477 
786 0 |d ProQuest  |t Health & Medical Collection 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3146658221/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3146658221/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3146658221/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch