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Projeto de investigação
Emotional Regulation Assessment via multi-biosignal processing in a VR environment for neurorehabilitation.
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Automatic cognitive fatigue detection using wearable fNIRS and machine learning
Publication . Varandas, Rui; Lima, Rodrigo; Bermúdez i Badia, Sergi; Silva, Hugo; Gamboa, Hugo
Wearable sensors have increasingly been applied in healthcare to generate data and monitor
patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtru sively monitoring one’s cognitive state over time. A particular state relevant in multiple domains
is cognitive fatigue, which may impact performance and attention, among other capabilities. The
monitoring of this state will be applied in real learning settings to detect and advise on effective
break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices
were employed to build a BCI to automatically detect the state of cognitive fatigue using machine
learning algorithms. An experimental procedure was developed to effectively induce cognitive
fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task
and a concentration task. Machine learning models were user-tuned to account for the individual
dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67%. We
concluded that, although effective for some subjects, the methodology needs to be individually
validated before being applied. Moreover, time on task was not a particularly determining factor
for classification, i.e., to induce cognitive fatigue. Further research will include other physiological
signals and human–computer interaction variables.
Emotional regulation assessment via multi-biosignal processing in a VR environment for neurorehabilitation
Publication . Lima, Rodrigo Olival; Bermúdez i Badia, Sergi; Gamboa, Hugo Filipe Silveira; Cameirão, MÛnica da Silva
Emerging immersive technologies and physiological computing capabilities are opening
promising pathways for emotion recognition and regulation, with growing relevance in
fields such as affective computing, neurorehabilitation, and human-computer interaction.
Through four exploratory studies, this thesis investigates how virtual reality, biofeed back, and machine learning can be combined to recognize and regulate users’ emotional
states in real time.
First, a machine learning pipeline was developed to classify emotional states using
physiological signals collected in immersive and non-immersive virtual reality conditions.
Results showed that immersion had a limited impact on subjective emotional ratings, while
user-dependent models significantly outperformed user-independent ones, highlighting
the importance of personalization in emotion recognition.
The second study validated this pipeline in individuals with Alzheimer’s, revealing
that emotional reactivity is partially preserved across severity levels. Classification models
successfully distinguished between emotional states, healthy and Alzheimer’s participants,
and even Alzheimer’s severity levels, underscoring the pipeline’s clinical relevance and
generalization.
The third study introduced a nature-based virtual reality environment, the Virtual Lev ada, which used real-time adaptation to users’ physiological stress levels via a biofeedback
mechanism. This study also implemented and evaluated real-time retraining strategies
for the stress classification model, addressing temporal drift and improving model robust ness. Although biofeedback effects were not statistically significant, both adaptive and
non-adaptive groups reported reduced physiological arousal and anxiety, supporting the
environment’s calming and restorative potential.
Finally, the fourth study improved the adaptive virtual reality system by integrating
online stress predictions and online model retraining. Results demonstrated improved
prediction stability over time and significant reductions in state anxiety, particularly in
individuals with elevated stress levels.
In conclusion, these findings validate the feasibility and effectiveness of progressively
adaptive, personalized virtual reality systems for emotion recognition and regulation.
This work contributes with novel insights into how online physiological monitoring and
ML adaptation can enhance emotional self-regulation, offering promising directions for
affective technologies development and mental health interventions.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
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Número da atribuição
2020.06024.BD