Deep learning-based multi-brain capsule network for Next-Gen Clinical Emotion recognition using EEG signals Article Swipe
Ravinder Dahiya
,
G Mamatha
,
Shila Jawale
,
Santanu Das
,
Sagar Choudhary
,
Vinod Rathod
,
Bhawna Janghel Rajput
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.neuri.2025.100203
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.neuri.2025.100203
Deep learning techniques are crucial for next-generation clinical applications, particularly in Next-Gen Clinical Emotion recognition. To enhance classification accuracy, we propose an Attention mechanism based Capsule Network Model (At-CapNet) for Multi-Brain Region. EEG-tNIRS signals were collected using Next-Gen Clinical Emotion-inducing visual stimuli to construct the TYUT3.0 dataset, from which EEG and tNIRS features were extracted and mapped into matrices. A multi-brain region attention mechanism was applied to integrate EEG and tNIRS features, assigning different weights to features from distinct brain regions to obtain high-quality primary capsules. Additionally, a capsule network module was introduced to optimize the number of capsules entering the dynamic routing mechanism, improving computational efficiency. Experimental validation on the TYUT3.0 Next-Gen Clinical Emotion dataset demonstrates that integrating EEG and tNIRS improves recognition accuracy by 1.53% and 14.35% compared to single-modality signals. Moreover, the At-CapNet model achieves an average accuracy improvement of 4.98% over the original CapsNet model and outperforms existing CapsNet-based Next-Gen Clinical Emotion recognition models by 1% to 5%. This research contributes to the advancement of non-invasive neurotechnology for precise Next-Gen Clinical Emotion recognition, with potential implications for next-generation clinical diagnostics and interventions.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1016/j.neuri.2025.100203
- OA Status
- gold
- Cited By
- 5
- References
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- OpenAlex ID
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https://openalex.org/W4409886107Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.neuri.2025.100203Digital Object Identifier
- Title
-
Deep learning-based multi-brain capsule network for Next-Gen Clinical Emotion recognition using EEG signalsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-28Full publication date if available
- Authors
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Ravinder Dahiya, G Mamatha, Shila Jawale, Santanu Das, Sagar Choudhary, Vinod Rathod, Bhawna Janghel RajputList of authors in order
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https://doi.org/10.1016/j.neuri.2025.100203Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.neuri.2025.100203Direct OA link when available
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Electroencephalography, Computer science, Deep learning, Pattern recognition (psychology), Artificial intelligence, Neuroscience, PsychologyTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 5Per-year citation counts (last 5 years)
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53Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.assigning | 72 |
| abstract_inverted_index.attention | 62 |
| abstract_inverted_index.capsules. | 85 |
| abstract_inverted_index.collected | 35 |
| abstract_inverted_index.construct | 43 |
| abstract_inverted_index.different | 73 |
| abstract_inverted_index.extracted | 54 |
| abstract_inverted_index.features, | 71 |
| abstract_inverted_index.improving | 104 |
| abstract_inverted_index.integrate | 67 |
| abstract_inverted_index.matrices. | 58 |
| abstract_inverted_index.mechanism | 23, 63 |
| abstract_inverted_index.potential | 178 |
| abstract_inverted_index.introduced | 92 |
| abstract_inverted_index.mechanism, | 103 |
| abstract_inverted_index.techniques | 2 |
| abstract_inverted_index.validation | 108 |
| abstract_inverted_index.(At-CapNet) | 28 |
| abstract_inverted_index.Multi-Brain | 30 |
| abstract_inverted_index.advancement | 167 |
| abstract_inverted_index.contributes | 164 |
| abstract_inverted_index.diagnostics | 183 |
| abstract_inverted_index.efficiency. | 106 |
| abstract_inverted_index.improvement | 141 |
| abstract_inverted_index.integrating | 118 |
| abstract_inverted_index.multi-brain | 60 |
| abstract_inverted_index.outperforms | 150 |
| abstract_inverted_index.recognition | 123, 156 |
| abstract_inverted_index.Experimental | 107 |
| abstract_inverted_index.demonstrates | 116 |
| abstract_inverted_index.high-quality | 83 |
| abstract_inverted_index.implications | 179 |
| abstract_inverted_index.non-invasive | 169 |
| abstract_inverted_index.particularly | 9 |
| abstract_inverted_index.recognition, | 176 |
| abstract_inverted_index.recognition. | 14 |
| abstract_inverted_index.Additionally, | 86 |
| abstract_inverted_index.CapsNet-based | 152 |
| abstract_inverted_index.applications, | 8 |
| abstract_inverted_index.computational | 105 |
| abstract_inverted_index.classification | 17 |
| abstract_inverted_index.interventions. | 185 |
| abstract_inverted_index.neurotechnology | 170 |
| abstract_inverted_index.next-generation | 6, 181 |
| abstract_inverted_index.single-modality | 131 |
| abstract_inverted_index.Emotion-inducing | 39 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.98594691 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |