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

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

---

Combining VR with electroencephalography as a frontier of brain-computer interfaces

Device, Journal Year: 2024, Volume and Issue: 2(6), P. 100425 - 100425

Published: June 1, 2024

This review presents an overview of the integration virtual reality (VR) and electroencephalography (EEG), known as VR-EEG systems, their promising applications brain-computer interfaces (BCIs), including motor cognitive rehabilitation, entertainment, education. We outline progress thus far highlight challenges still faced, such hair compatibility, seamless EEG sensors VR headsets, limited recording sites signal quality. also points out areas requiring advancements, development electrodes, multimodal closed-loop for providing a more tailored, immersive BCI experience.

Language: Английский

---

Navigating the 'Zen Zeitgeist': The Potential of Personalized Neurofeedback for Meditation

Published: Dec. 5, 2023

The advancement of neurotechnological tools for meditation and mindfulness training may help to accelerate many the transformational states traits that result from consistent practice. However, adopting a traditional one-size-fits-all approach in development tools, such as neurofeedback applications training, will likely limit potential benefits; individual differences compensatory mechanisms strongly impact both efficacy given protocol, well how foundational skills are acquired. Here we emphasize importance embracing propose novel, personalized intervention technologies sidestep potentially deleterious outcomes. Given growing interest research on effects brain, behavior, overall health, briefly address some philosophical cultural challenges associated with translating contemplative practices into applications, further accentuating need individualized multimodal approaches.

Language: Английский

---

Evaluation of Theta EEG Neurofeedback Procedure for Cognitive Training Using Simultaneous fMRI in Counterbalanced Active‐Sham Study Design

Human Brain Mapping, Journal Year: 2025, Volume and Issue: 46(1)

Published: Jan. 1, 2025

ABSTRACT Evaluation of mechanisms action EEG neurofeedback (EEG‐nf) using simultaneous fMRI is highly desirable to ensure its effective application for clinical rehabilitation and therapy. Counterbalancing training runs with active sham (neuro)feedback each participant a promising approach demonstrate specificity effects the neurofeedback. We report first study in which EEG‐nf procedure both evaluated controlled via counterbalanced active‐sham design. Healthy volunteers ( n = 18) used upregulate frontal theta asymmetry (FTA) during while performing tasks that involved mental generation random numerical sequence serial summation numbers sequence. The FTA was defined as power channels F3 F4 [4–7] Hz band. Sham feedback provided based on motion‐related artifacts. experimental included two feedback, randomized order. participants showed significantly more positive changes conditions compared conditions, associated higher channel F3. Temporal correlations between activities prefrontal, parietal, occipital brain regions were enhanced conditions. correlation activity left dorsolateral prefrontal cortex (DLPFC) also enhanced. Significant active‐vs‐sham difference activations observed DLPFC. Our results can be reliably design fMRI.

Language: Английский

---

Neurological benefits of third places for young adults in healthy urban environments

Frontiers of Architectural Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

---

The Lack of Neurofeedback Training Regulation Guidance and Process Evaluation May be a Source of Controversy in Post-Traumatic Stress Disorder–Neurofeedback Research: A Systematic Review and Statistical Analysis

Brain Connectivity, Journal Year: 2025, Volume and Issue: unknown

Published: May 15, 2025

Objectives: Neurofeedback (NF) based on brain-computer interface (BCI) is an important direction in adjunctive interventions for post-traumatic stress disorder (PTSD). However, existing research lacks comprehensive methodologies and experimental designs. There are concerns the field regarding effectiveness mechanistic interpretability of NF, prompting this study to conduct a systematic analysis primary NF techniques outcomes PTSD modulation. The aims explore reasons behind these propose directions addressing them. Methods: A search conducted Web Science database up December 1, 2023, yielded 111 English articles, which 80 were excluded predetermined criteria irrelevant study. remaining 31 original studies included literature review. checklist was developed assess robustness credibility studies. Subsequently, classified into electroencephalogram-based (EEG-NF) functional magnetic resonance imaging-based (fMRI-NF) BCI type. Data target brain regions, signals, modulation protocols, control group types, assessment methods, data processing strategies, reported extracted synthesized. Consensus theories from future improvements related distilled. Results: Analysis all revealed that average sample size patients EEG fMRI 17.4 (SD 7.13) 14.6 6.37), respectively. Due neurofeedback training protocol constraints, 93% EEG-NF 87.5% fMRI-NF used traditional statistical with minimal utilization basic machine learning (ML) methods no utilizing deep (DL) methods. Apart approximately 25% supporting exploratory psychoregulatory lacked explicit guidance. Only 13% evaluated involving signal classification, decoding during process, lacking process monitoring means. Conclusion: In summary, holds promise as intervention technique PTSD, potentially aiding symptom alleviation patients. necessary evaluation mechanisms PTSD-NF, clarity guidance, development ML/DL suitable PTSD-NF small sizes. To address challenges, it crucial adopt more rigorous should focus integration advanced enhance precision interventions. Impact Statement implications limited application (NFT) (PTSD) research, where significant portion approaches, foundational conclusions lack consensus. notable absence retrospective analyses NFT PTSD. This provides discussion offering valuable insights findings hold significance researchers, clinicians, practitioners field, providing foundation informed, evidence-based treatment.

Language: Английский

---

Non-linear processing and reinforcement learning to predict rTMS treatment response in depression

Psychiatry Research Neuroimaging, Journal Year: 2023, Volume and Issue: 337, P. 111764 - 111764

Published: Nov. 23, 2023

Language: Английский

---

Attention-based Temporal Graph Representation Learning for EEG-based Emotion Recognition

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2024, Volume and Issue: 28(10), P. 5755 - 5767

Published: May 2, 2024

Due to the objectivity of emotional expression in central nervous system, EEG-based emotion recognition can effectively reflect humans' internal states. In recent years, convolutional neural networks (CNNs) and recurrent (RNNs) have made significant strides extracting local features temporal dependencies from EEG signals. However, CNNs ignore spatial distribution information electrodes; moreover, RNNs may encounter issues such as exploding/vanishing gradients high time consumption. To address these limitations, we propose an attention-based graph representation network (ATGRNet) for recognition. Firstly, a hierarchical attention mechanism is introduced integrate feature representations both frequency bands channels ordered by priority Second, with top-k operation utilized capture relationships between electrodes under different patterns. Next, residual-based readout applied accumulate node-level into graph-level representations. Finally, obtained are fed (TCN) extract frames. We evaluated our proposed ATGRNet on SEED, DEAP FACED datasets. The experimental findings show that surpasses state-of-the-art graph-based mehtods

Language: Английский

---

Application of Neuroscience Methods in HRDM for Brain-Based Human Capital Optimization

Advances in human resources management and organizational development book series, Journal Year: 2024, Volume and Issue: unknown, P. 227 - 261

Published: April 26, 2024

For years, human resource development and management (HRDM) has used behavioral assessments to gauge employee potential. However, advancements in cognitive neuroscience (CBN) have opened up new possibilities for understanding how the mind works. This chapter explores practical applications of methods like EEG, ERP, MRI, fMRI, as well neurofeedback biofeedback, talent identification, leadership development, well-being. Importantly, these insights can be directly applied HRDM practices, leading more effective management, improved While recognizing ethical considerations involved with technologies, presents a compelling vision future where practices are informed by deeper brain, enabling workforce reach its full

Language: Английский

---

Sensori-motor neurofeedback improves inhibitory control and induces neural changes: a placebo-controlled, double-blind, event-related potentials study

International Journal of Clinical and Health Psychology, Journal Year: 2024, Volume and Issue: 24(3), P. 100501 - 100501

Published: July 1, 2024

Language: Английский

---