mental fatigue
Research Papers
Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022)
Mental fatigue is a psychophysical condition with a significant adverse effect on daily life, compromising both physical and mental wellness. We are experiencing challenges in this fast-changing environment, and mental fatigue problems are becoming more prominent. This demands an urgent need to explore an effective and accurate automated system for timely mental fatigue detection. Therefore, we present a systematic review of brain-computer interface (BCI) studies for mental fatigue detection using artificial intelligent (AI) techniques published in Scopus, IEEE Explore, PubMed and Web of Science (WOS) between 2011 and 2022. The Boolean search expression that comprised (((ELECTROENCEPHALOGRAM) AND (BCI)) AND (FATIGUE CLASSIFICATION)) AND (BRAIN-COMPUTER INTERFACE) has been used to select the articles. Through the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, we selected 39 out of 562 articles. Our review identified the research gap in employing BCI for mental fatigue intervention through automated neurofeedback. The AI techniques employed to develop EEG-based mental fatigue detection are also discussed. We have presented comprehensive challenges and future recommendations from the gaps identified in discussions. The future direction includes data fusion, hybrid classification models, availability of public datasets, uncertainty, explainability, and hardware implementation strategies.
View Full Paper →A Novel Brain–Computer Interface Virtual Environment for Neurofeedback During Functional MRI
Virtual environments (VEs), in the recent years, have become more prevalent in neuroscience. These VEs can offer great flexibility, replicability, and control over the presented stimuli in an immersive setting. With recent developments, it has become feasible to achieve higher-quality visuals and VEs at a reasonable investment. Our aim in this project was to develop and implement a novel real-time functional magnetic resonance imaging (rt-fMRI)–based neurofeedback (NF) training paradigm, taking into account new technological advances that allow us to integrate complex stimuli into a visually updated and engaging VE. We built upon and developed a first-person shooter in which the dynamic change of the VE was the feedback variable in the brain–computer interface (BCI). We designed a study to assess the feasibility of the BCI in creating an immersive VE for NF training. In a randomized single-blinded fMRI-based NF-training session, 24 participants were randomly allocated into one of two groups: active and reduced contingency NF. All participants completed three runs of the shooter-game VE lasting 10 min each. Brain activity in a supplementary motor area region of interest regulated the possible movement speed of the player’s avatar and thus increased the reward probability. The gaming performance revealed that the participants were able to actively engage in game tasks and improve across sessions. All 24 participants reported being able to successfully employ NF strategies during the training while performing in-game tasks with significantly higher perceived NF control ratings in the NF group. Spectral analysis showed significant differential effects on brain activity between the groups. Connectivity analysis revealed significant differences, showing a lowered connectivity in the NF group compared to the reduced contingency-NF group. The self-assessment manikin ratings showed an increase in arousal in both groups but failed significance. Arousal has been linked to presence, or feelings of immersion, supporting the VE’s objective. Long paradigms, such as NF in MRI settings, can lead to mental fatigue; therefore, VEs can help overcome such limitations. The rewarding achievements from gaming targets can lead to implicit learning of self-regulation and may broaden the scope of NF applications.
View Full Paper →EEG-Based Tool for Prediction of University Students’ Cognitive Performance in the Classroom
This study presents a neuroengineering-based machine learning tool developed to predict students’ performance under different learning modalities. Neuroengineering tools are used to predict the learning performance obtained through two different modalities: text and video. Electroencephalographic signals were recorded in the two groups during learning tasks, and performance was evaluated with tests. The results show the video group obtained a better performance than the text group. A correlation analysis was implemented to find the most relevant features to predict students’ performance, and to design the machine learning tool. This analysis showed a negative correlation between students’ performance and the (theta/alpha) ratio, and delta power, which are indicative of mental fatigue and drowsiness, respectively. These results indicate that users in a non-fatigued and well-rested state performed better during learning tasks. The designed tool obtained 85% precision at predicting learning performance, as well as correctly identifying the video group as the most efficient modality.
View Full Paper →Differential effects on mood of 12-15 (SMR) and 15-18 (beta1) Hz neurofeedback
The common assumption in EEG-neurofeedback is one of functional specificity of the trained spectral bands, though it has been posited that only a nonspecific generalised learning process may be engaged. Earlier we reported differential effects on attention in healthy participants measured with continuous performance tests and the P300, following training of the sensory-motor rhythm band (SMR, 12-15 Hz) compared with the adjacent beta1 (15-18 hz) band. Here previously unreported results are presented with phenomenological data from an activation checklist in support of the putative calming effect of SMR neurofeedback. While within sessions both protocols induced tiredness, this was paralleled by an increase in calmness only following SMR training. The differential effect on mood was theoretically consistent and extends evidence of cognitive functional specificity with neurofeedback to affective processes.
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