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Review
. 2025 Feb 13;15(4):456.
doi: 10.3390/diagnostics15040456.

Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations

Constantinos Halkiopoulos  1 , Evgenia Gkintoni  2 , Anthimos Aroutzidis  1 , Hera Antonopoulou  1
Affiliations
Review

Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations

Constantinos Halkiopoulos et al. Diagnostics (Basel). .

Abstract

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

Keywords: brain-computer interaction; cognitive neuroscience; deep learning; emotion detection; emotion recognition; neural networks; neuroimaging.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Network visualization of neuroimaging and deep learning integration.
Figure 2
Figure 2
Flowchart of PRISMA methodology.
Figure 3
Figure 3
Risk of bias assessment visualization.
Figure 4
Figure 4
Comparison of neuroimaging modalities for emotion detection.
Figure 5
Figure 5
Real-time emotion tracking via neurofeedback and deep learning.
Figure 6
Figure 6
Functional neuroanatomy of emotion networks.
Figure 7
Figure 7
Temporal dynamics of emotion processing.
Figure 8
Figure 8
Structured visualization of emotion detection in adaptive systems.
Figure 9
Figure 9
Performance funnel for 64 studies.
Figure 10
Figure 10
Accuracy (%) across neuroimaging modalities and AI models.
Figure 11
Figure 11
Interpretability (%) across neuroimaging modalities and AI models.
Figure 12
Figure 12
Feasibility (%) across neuroimaging modalities and AI models.
See this image and copyright information in PMC

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