Perceiving Etruscan Art: AI and Visual Perception DOI Creative Commons
Maurizio Forte

Humans, Journal Year: 2024, Volume and Issue: 4(4), P. 409 - 429

Published: Dec. 18, 2024

This research project is aimed at exploring the cognitive and emotional processes involved in perceiving Etruscan artifacts. The case study Sarcophagus of Spouses National Museum Rome, one most important masterpieces pre-Roman art. utilized AI eye-tracking technology to analyze how viewers engaged with Spouses, revealing key patterns visual attention engagement. OpenAI, ChatGPT-4 (accessed on 12 October 2024) was used conjunction Colab–Python order elaborate all spreadsheets data arising from recording. results showed that primarily focused central figures, especially their faces hands, indicating a high level interest human elements artifact. longer fixation duration these features suggest find them particularly engaging, which likely due detailed craftsmanship symbolic significance. also highlighted specific gaze patterns, such as diagonal scanning across sarcophagus, reflects composition’s ability guide viewer strategically. indicate focus centers elements, suggesting hold both esthetic

Language: Английский

Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations DOI Creative Commons
Constantinos Halkiopoulos, Evgenia Gkintoni,

Anthimos Aroutzidis

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(4), P. 456 - 456

Published: Feb. 13, 2025

Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights advanced algorithmic methods in pursuit of an enhanced understanding and applications recognition. Methods: study was conducted PRISMA guidelines, involving a rigorous selection process that resulted the inclusion 64 empirical studies explore modalities such as fMRI, EEG, MEG, discussing their capabilities limitations It further evaluates architectures, including neural networks, CNNs, GANs, terms roles classifying emotions from various domains: human-computer interaction, mental health, marketing, more. Ethical practical challenges implementing these systems are also analyzed. Results: identifies fMRI powerful but resource-intensive modality, while EEG MEG more accessible high temporal resolution limited by spatial accuracy. Deep models, especially CNNs have performed well emotions, though they do not always require large diverse datasets. Combining data behavioral features improves classification performance. However, ethical challenges, privacy bias, remain significant concerns. Conclusions: has emphasized efficiencies detection, technical were highlighted. Future research should integrate advances, establish innovative enhance system reliability applicability.

Language: Английский

Citations

6

From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications DOI Creative Commons
Evgenia Gkintoni,

Anthimos Aroutzidis,

Hera Antonopoulou

et al.

Brain Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 220 - 220

Published: Feb. 20, 2025

Background/Objectives: This systematic review presents how neural and emotional networks are integrated into EEG-based emotion recognition, bridging the gap between cognitive neuroscience practical applications. Methods: Following PRISMA, 64 studies were reviewed that outlined latest feature extraction classification developments using deep learning models such as CNNs RNNs. Results: Indeed, findings showed multimodal approaches practical, especially combinations involving EEG with physiological signals, thus improving accuracy of classification, even surpassing 90% in some studies. Key signal processing techniques used during this process include spectral features, connectivity analysis, frontal asymmetry detection, which helped enhance performance recognition. Despite these advances, challenges remain more significant real-time processing, where a trade-off computational efficiency limits implementation. High cost is prohibitive to use real-world applications, therefore indicating need for development application optimization techniques. Aside from this, obstacles inconsistency labeling emotions, variation experimental protocols, non-standardized datasets regarding generalizability recognition systems. Discussion: These developing adaptive, algorithms, integrating other inputs like facial expressions sensors, standardized protocols elicitation classification. Further, related ethical issues respect privacy, data security, machine model biases be much proclaimed responsibly apply research on emotions areas healthcare, human–computer interaction, marketing. Conclusions: provides critical insight suggestions further field toward robust, scalable, applications by consolidating current methodologies identifying their key limitations.

Language: Английский

Citations

3

Neurodevelopmental Pathways from Temperamental Fear to Anxiety DOI
Eun-Kyung Shin, Koraly Pérez‐Edgar

Current topics in behavioral neurosciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0

Machine Learning Models for Predicting Emotional Valence from Brain Activity and Physiological Responses DOI
Ali Salman, Alessio Luschi, Ernesto Iadanza

et al.

IFMBE proceedings, Journal Year: 2025, Volume and Issue: unknown, P. 461 - 469

Published: Jan. 1, 2025

Language: Английский

Citations

0

Perceiving Etruscan Art: AI and Visual Perception DOI Creative Commons
Maurizio Forte

Humans, Journal Year: 2024, Volume and Issue: 4(4), P. 409 - 429

Published: Dec. 18, 2024

This research project is aimed at exploring the cognitive and emotional processes involved in perceiving Etruscan artifacts. The case study Sarcophagus of Spouses National Museum Rome, one most important masterpieces pre-Roman art. utilized AI eye-tracking technology to analyze how viewers engaged with Spouses, revealing key patterns visual attention engagement. OpenAI, ChatGPT-4 (accessed on 12 October 2024) was used conjunction Colab–Python order elaborate all spreadsheets data arising from recording. results showed that primarily focused central figures, especially their faces hands, indicating a high level interest human elements artifact. longer fixation duration these features suggest find them particularly engaging, which likely due detailed craftsmanship symbolic significance. also highlighted specific gaze patterns, such as diagonal scanning across sarcophagus, reflects composition’s ability guide viewer strategically. indicate focus centers elements, suggesting hold both esthetic

Language: Английский

Citations

1