Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T13:47:06.245681Z
Paper Citation Record · LEDGER
As of 16 August 2026, this Paper Citation Record lists 100 of 103 outbound references and 0 inbound Pith citation observations for arXiv:2512.23076.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: paper_references, paper_reference_links, observed 2026-08-03T13:47:06.245681Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
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100 of 103 outbound references displayed
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Observation 61a55a4e-b057-487d-968c-d3127a168b55 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Eeg emotion recognition using dynamical graph convolutional neural networks,
Reference 1
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Observation cd5048fe-a408-4613-92b3-21cce4641de2 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition The effi- cacy of emotion recognition rehabilitation for people with alzheimer’s disease,
Reference 2
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Observation 3baa4cb0-278a-4489-a919-745b888abe58 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Emotion recognition using eeg and physiological data for robot-assisted rehabilitation systems,
Reference 3
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Observation 28fc1cd6-a233-481b-b132-240f11c07cf6 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Divergent spatiotemporal signatures characterize impaired facial emotional recognition in major depressive disorder: An event-related microstate study,
Reference 4
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Observation 4dcb5182-30ea-46de-afa6-44d40530ef27 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition A real-time automated system for the recognition of human facial expressions,
Reference 5
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Observation f326a50f-cd7c-4c58-a31e-ed2b69db47ba · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Sponta- neous facial micro-expression analysis using spatiotemporal completed local quantized patterns,
Reference 6
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Observation 98087266-6765-4f60-8e67-0826daab3086 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Facial micro- expression recognition using spatiotemporal local binary pattern with integral projection,
Reference 7
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Observation b2f0d905-8a93-46ba-b3c1-9a766b51e65a · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Discriminative spatiotemporal local binary pattern with revisited in- tegral projection for spontaneous facial micro-expression recognition,
Reference 8
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Observation 25594a82-5867-439c-a4a3-abee5567fbe9 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition A main directional mean optical flow feature for spontaneous micro- expression recognition,
Reference 9
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Observation ee2ed8e5-72ff-4041-b8ec-ec85c4943937 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Integrating facial expression and body gesture in videos for emotion recognition,
Reference 10
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Observation 8567e243-cff5-4edb-bc46-013f79f7e452 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Prosody- based automatic detection of annoyance and frustration in human- computer dialog,
Reference 11
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Observation 17fdff78-8026-4f36-b164-de79d09c9906 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Multichannel EEG-based emotion recognition via group sparse canonical correlation analysis,
Reference 12
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Observation 7816bb7e-8ece-49e5-ac8d-272c6ce3d70a · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Emotion recognition from surface EMG signal using wavelet transform and neural network,
Reference 13
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Observation c3316105-9471-4e27-943c-3d7d6ee1fc67 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition ECG pattern analysis for emotion detection,
Reference 14
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Observation 212c69a2-61ca-41da-a8a5-d8882df45558 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Hyper- complex multimodal emotion recognition from eeg and peripheral phys- iological signals,
Reference 15
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Observation 6e82b4ca-5189-4012-8f25-77237d35e3c5 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Measuring emotion: The self-assessment manikin and the semantic differential,
Reference 16
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Observation d39b0971-7245-4bfc-8d59-8ba94c3dc09f · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition A circumplex model of affect,
Reference 17
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Observation 861114e4-db4a-4c66-9a29-345f7dc7f30a · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition The circumplex model of affect: An integrative approach to affective neuroscience, cognitive de- velopment, and psychopathology,
Reference 18
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Observation 5e011438-4142-49bc-9b78-8254de52e3a1 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Pleasure–arousal–dominance: A general framework for describing and measuring individual differences in temperament,
Reference 19
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Observation 936fbbc3-b9d5-4f2a-83c1-22b48e1af926 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Affect detection: An interdisciplinary review of models, methods, and their applications,
Reference 20
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Observation 74a5aa84-3528-433b-a8b0-69fbb4816c79 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Deap: A database for emotion analysis; using physiological signals,
Reference 21
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Observation 8360c70d-220f-45a7-9fc4-9ea5e6ca6a00 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Ceap-360vr: A continuous physiological and behavioral emotion annotation dataset for 360◦ vr videos,
Reference 22
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Observation 345bdaca-42e1-461f-9296-40d6307ead5d · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Amigos: A dataset for affect, personality and mood research on individuals and groups,
Reference 23
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Observation 8d4a37e6-6b4f-46fb-9e89-29e828ad33fe · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Emotion recognition for everyday life using physiological signals from wearables: A systematic literature review,
Reference 24
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Observation c1b978c6-0396-4921-987b-c64f68e44c64 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Toward cross-subject and cross-session general- ization in eeg-based emotion recognition: Systematic review, taxonomy, and methods,
Reference 25
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Observation 80bd251e-a9d6-476b-a59f-ecf0b1914277 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition A comprehensive re- view of deep learning in eeg-based emotion recognition: classifications, trends, and practical implications,
Reference 26
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Observation 621d7569-51f5-4a16-ba45-72ce3e61a00a · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Autonomic nervous system activity in emotion: A review,
Reference 27
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Observation 0345456a-b220-42c5-b1da-e4495df0e8da · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Eeg-based multimodal emotion recognition: A machine learning per- spective,
Reference 28
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Observation ff91521a-e89d-4622-8984-34c718d0e1d5 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Multi- modal machine learning approach for emotion recognition using phys- iological signals,
Reference 29
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Observation a5dd1fd0-6c23-43a1-b2a7-03c0b9c8c9bd · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Advances in multimodal emotion recognition based on brain-computer interfaces,
Reference 30
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Observation fb9c8293-ba3a-4bc3-86e8-6e1d30341c7f · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Hierarchical hypercomplex network for multimodal emotion recognition,
Reference 31
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Observation 75c44de2-71fa-4a86-9c31-91d2866d0149 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Corrfeat: correlation-based feature extraction algorithm using skin conductance and pupil diameter for emotion recognition,
Reference 32
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Observation 25194ffc-0dba-4a5b-a69b-d7f828aa3e85 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Behavioral and physiological signals- based deep multimodal approach for mobile emotion recognition,
Reference 33
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Observation a9572e03-453c-43ff-84d2-3387ca38fd78 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Multimodal learning for eeg-based emotion recognition,
Reference 34
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Observation 0fbb90aa-c944-4c6b-b908-a460542cc044 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Barlow twins: Self-supervised learning via redundancy reduction,
Reference 35
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Observation 856c2bdf-036a-400c-a30d-7f80c167622c · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition VICReg: Variance-invariance- covariance regularization for self-supervised learning,
Reference 36
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Observation 6ddc74f6-50f7-4416-a944-ac3526797b24 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognition
Reference 37
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Observation 066e89f5-61fb-4ac0-86ac-e93371d37d0a · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition A simple framework for contrastive learning of visual representations,
Reference 38
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Observation dba63c11-65eb-40e5-9bf5-1398dc9d0b36 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Representation Learning with Contrastive Predictive Coding
Reference 39
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Observation 27a1a069-8cab-4c0a-b50a-6b8fc2c0a72e · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Learning transferable visual models from natural language supervision,
Reference 40
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Observation 788e2417-96bb-46b2-9007-4675fb9ad657 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text,
Reference 41
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Observation 77ae6b36-bb38-43b6-8822-75c658be2d25 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Imagebind: One embedding space to bind them all,
Reference 42
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Observation 33c90bc0-b5a9-4508-8043-d724d2da38ea · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Contrasting with symile: Simple model-agnostic representation learning for unlim- ited modalities,
Reference 43
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Observation da8ba59f-757f-4793-af8b-4a947c658d84 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Gramian multimodal representation learning and alignment,
Reference 44
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Observation ec72d01a-3e57-4c7b-8a73-2ba9a620d78e · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Applications of self-supervised learning to biomedical signals: A survey,
Reference 45
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Observation 12460427-d4ca-4ae5-8812-69b5c12f8b68 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Self- supervised eeg emotion recognition models based on cnn,
Reference 46
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Observation 7a8f91bb-f2ab-4ae2-8040-acf6d99d6f3d · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Self-supervised elec- troencephalogram representation learning for automatic sleep staging: model development and evaluation study,
Reference 47
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Observation 397f9974-4d41-4b26-b37e-a74271720859 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Self supervised learning based emotion recog- nition using physiological signals,
Reference 48
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Observation 1a32222b-b0ac-46cd-a688-3514918a81ed · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Cross-modal contrastive learning for emotion recognition: Aligning ecg with eeg-derived features,
Reference 49
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Observation e219a158-fbfb-4f27-8b81-f674d00cbd2b · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Linear dependence structure of the entropy space,
Reference 50
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Observation caec0909-abc6-4931-bbe6-66e3d1dec3a9 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Measuring dependence with matrix-based entropy functional,
Reference 51
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Observation 243c4b1c-e686-4eef-8286-206919ed3ad9 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition The Normalized Cross Density Functional: A Framework to Quantify Statistical Dependence for Random Processes
Reference 52
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Observation ed01b249-fd85-41e8-a854-6b3080891c3c · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Eeg analysis based on time domain properties,
Reference 53
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Observation f201f960-6168-4a24-aac0-98ec7cff10ee · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Emotion recognition from eeg using higher order crossings,
Reference 54
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Observation 61683007-e165-4040-8474-389bbd626e89 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Differential entropy feature for eeg-based vigilance estimation,
Reference 55
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Observation 390ba4fc-d182-4bde-b39c-7b25e79501df · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Emotion classification based on gamma-band eeg,
Reference 56
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Observation 333c3a3e-637b-407f-8eba-9347d1bb119d · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition What does the prefrontal cortex “do
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Observation 9e7633c1-f33d-4370-8682-f7f9ef13c7eb · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition A comparative study of window sizes for cnn-based eeg emotion recognition,
Reference 58
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Observation ba7318ba-19dc-41e3-9e2c-f68e9fcbab3e · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition An empirical survey of data augmentation for time series classification with neural networks,
Reference 59
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Observation 5790d992-73fc-4c87-98a3-9f4997040940 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Eeg-based emotion recognition using multi-scale dynamic cnn and gated transformer,
Reference 60
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Multimodal Functional Maximum Correlation for Emotion Recognition A survey on contrastive self-supervised learning,
Reference 61
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Observation a8f9aa86-6033-428d-9051-74d9e8ad763e · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Contrastive self-supervised learning for neurodegenerative disorder classification,
Reference 62
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Observation 8d3b87a7-4790-43fd-8d65-7d2a136d18bf · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Ganser: A self-supervised data augmentation framework for eeg-based emotion recognition,
Reference 63
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Observation 8d8a4915-3509-4155-9299-483baa294bff · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Self-supervised learning via vicreg enables training of emg pattern recognition us- ing continuous data with unclear labels,
Reference 64
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Observation 6b487e4e-cfd6-4605-a40e-72df36a8c609 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Dreamer: A database for emotion recognition through eeg and ecg signals from wireless low-cost off- the-shelf devices,
Reference 65
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Observation 7170e463-d1d0-4386-acfb-d3c61b9d4090 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition GANSER: A Self-Supervised Data Augmentation Framework for EEG-Based Emotion Recognition,
Reference 66
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Observation 16ca7b91-d501-400d-9149-29408ee69d4b · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Emotion recognition through combining eeg and eog over relevant channels with optimal windowing,
Reference 67
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Multimodal Functional Maximum Correlation for Emotion Recognition Multi-to-single: Reducing multimodal dependency in emotion recognition through contrastive learning,
Reference 68
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Multimodal Functional Maximum Correlation for Emotion Recognition Accommo- dating audio modality in clip for multimodal processing,
Reference 69
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Multimodal Functional Maximum Correlation for Emotion Recognition Multivariate information transmission,
Reference 70
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Multimodal Functional Maximum Correlation for Emotion Recognition Information theoretical analysis of multivariate correla- tion,
Reference 71
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Multimodal Functional Maximum Correlation for Emotion Recognition Synergy, re- dundancy, and multivariate information measures: an experimentalist’s perspective,
Reference 72
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Observation 5f1c8264-cdc1-4e3f-a2de-c64e6631fd7d · outbound
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Reference 73
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Multimodal Functional Maximum Correlation for Emotion Recognition Formal limitations on the measurement of mutual information,
Reference 74
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Multimodal Functional Maximum Correlation for Emotion Recognition Universal features for high-dimensional learning and inference,
Reference 75
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Multimodal Functional Maximum Correlation for Emotion Recognition Multi-variate correlation and mixtures of product measures
Reference 76
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Multimodal Functional Maximum Correlation for Emotion Recognition Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks,
Reference 77
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Observation 99b35436-b19e-4b3d-abe3-1d375e264928 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition A Multimodal Database for Affect Recognition and Implicit Tagging (MAHNOB- HCI),
Reference 78
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Observation 9fc33cbc-c7f2-4187-8f5c-55975063476c · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Multimodal emotion detection via attention-based fusion of facial and speech features,
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Multimodal Functional Maximum Correlation for Emotion Recognition Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces,
Reference 80
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Multimodal Functional Maximum Correlation for Emotion Recognition Pytorch: An imperative style, high- performance deep learning library,
Reference 81
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Multimodal Functional Maximum Correlation for Emotion Recognition Measures of entropy from data using infinitely divisible kernels,
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Multimodal Functional Maximum Correlation for Emotion Recognition Emotion recog- nition for everyday life using physiological signals from wearables: A systematic literature review,
Reference 83
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Multimodal Functional Maximum Correlation for Emotion Recognition Unresolved cited work
Reference 84
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Multimodal Functional Maximum Correlation for Emotion Recognition Unresolved cited work
Reference 85
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Observation c389cd8c-f2c0-4ef7-811e-33cb8388c965 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Very short windows (<3s) yield a drop of≥8pp in macro-F 1, indicating insufficient temporal context
Reference 86
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Multimodal Functional Maximum Correlation for Emotion Recognition The three benchmarks supply more physiological signals than are strictly necessary for emotion recognition
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Observation 0321896b-cd2c-4d53-9004-a20d2edf9202 · outbound
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Reference 88
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Observation c3d49520-5bbe-4515-ac29-61b97ab12fa1 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition The mask assigns the largest weights to all 32 EEG leads, both horizontal and vertical EOG, and the skin-temperature trace, resulting in 35 retained inputs
Reference 89
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Observation 0920b722-88f9-40b7-a382-cd3f7a971d87 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition The attention mask ranks BVP, EDA and SKT highest; those three signals are therefore kept for downstream experiments
Reference 90
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Observation 53c3a2c7-4e77-4c6e-8571-c7174058bc19 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Opt.z-score
Reference 91
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Observation dc46a202-02ae-41ba-9cd9-7787f876188b · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition •VICReg.Identical pipeline to SimCLR (noise0.05, shift±10, dropout0.1)
Reference 92
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Observation f2e29c5b-595d-418c-a79d-a61a3cbf7a95 · outbound
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Reference 93
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Observation 5ba4e263-7bf8-41aa-8214-1db2067062fa · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition •VICReg.Same settings as SimCLR (noise0.05, shift±10, dropout0.1)
Reference 94
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Observation eb6c844d-a015-4d11-ba6d-e8be58b8fd9e · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Unresolved cited work
Reference 95
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Observation bcde571a-bafc-4f13-abc1-22e50092fbc4 · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Run the following once: •Unzip raw data:Ensure thats01.dat–s32.datreside in<BASE_PATH>/DEAP/
Reference 96
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Observation b33cd332-7c26-4966-8cba-9e8858e7ce2e · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition •Core scientific libraries:numpy 1.21.6,scipy 1.9.3,scikit-learn 1.1.3,matplotlib 3.5.3,joblib 1.2.0
Reference 97
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Observation 09e6f440-e6c0-4371-82d7-c13f08bb788c · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition HereN= 20 097, reflecting10s windows (1 280 samples at 128 Hz) extracted with a0.4s stride
Reference 98
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Observation 99158f06-8b5b-46f9-94ee-26efca669b5f · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition •Temporal networkShared four-block Conv→Batch Norm→ReLU→Max Pool backbone with filter widths 1→32 →64→128→256, kernel size 11, pooling factor 4 at each block
Reference 99
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Observation 7563984e-8ea8-4237-8bda-537a115b86fb · outbound
Multimodal Functional Maximum Correlation for Emotion Recognition Unresolved cited work
Reference 100
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No inbound Pith citation observations are available.