Готові списки джерел за темами / DEAP EEG

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Добірка наукової літератури з теми "DEAP EEG"

Автор: Grafiati
Опубліковано: 3 червня 2025
Оновлено: 31 липня 2025

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Статті в журналах з теми "DEAP EEG"

1

Oka, Hayato, Keiko Ono, and Adamidis Panagiotis. "Attention-Based PSO-LSTM for Emotion Estimation Using EEG." Sensors 24, no. 24 (2024): 8174. https://doi.org/10.3390/s24248174.

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Анотація:
Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation. This study presents a novel approach to enhance EEG-based emotion estimation accuracy by emphasizing temporal features and efficient parameter space exploration. We propose a model combining Long Short-Term Memory (LSTM) with an attention
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2

Anjum, Madiha, Wardah Batool, Raazia Saher, and Sanay Muhammad Umar Saeed. "Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features." Applied Sciences 14, no. 20 (2024): 9527. http://dx.doi.org/10.3390/app14209527.

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Анотація:
The analysis of stress in response to videos using electroencephalography (EEG) has emerged as a significant field of research. In this study, we propose a methodology for classifying stress responses to videos using the Database for Emotion Analysis using Physiological Signals (DEAP). EEG signals are preprocessed with resampling and a median filter. We extracted Power Spectral Density (PSD) features from the alpha, beta, delta, and theta bands of the preprocessed EEG. Instances were labeled based on the valence and arousal values provided in the DEAP dataset in response to the presented video
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3

Mahmoud, Aseel, Khalid Amin, Mohamad Mahmoud Al Rahhal, Wail S. Elkilani, Mohamed Lamine Mekhalfi, and Mina Ibrahim. "A CNN Approach for Emotion Recognition via EEG." Symmetry 15, no. 10 (2023): 1822. http://dx.doi.org/10.3390/sym15101822.

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Анотація:
Emotion recognition via electroencephalography (EEG) has been gaining increasing attention in applications such as human–computer interaction, mental health assessment, and affective computing. However, it poses several challenges, primarily stemming from the complex and noisy nature of EEG signals. Commonly adopted strategies involve feature extraction and machine learning techniques, which often struggle to capture intricate emotional nuances and may require extensive handcrafted feature engineering. To address these limitations, we propose a novel approach utilizing convolutional neural net
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4

Kanchi, Lohitha Lakshmi, Chandana Sri Narra, Guna Mantri, Madhupriya Palepogu, and Chandra Sekhar Reddy Mettu. "Emotion Recognition from Brain EEG Signals." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2036–43. http://dx.doi.org/10.22214/ijraset.2024.59255.

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Анотація:
Abstract: The recognition of emotions plays a vital role in various fields such as neuroscience, cognitive sciences, and biomedical engineering. This particular project is centered on the development of a system for recognizing emotions through EEG signals. The main goal is to accurately classify different emotional states like valence and arousal by analyzing EEG brain wave patterns. The study is based on the DEAP dataset, which contains EEG and peripheral physiological signals recorded as participants interacted with video clips and music. The main objective is to explore and compare the eff
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5

Kulkarni, Deepthi D., Vaibhav Vitthalrao Dixit, and Shweta Shirish Deshmukh. "Emotion detection using EEG: hybrid classification approach." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 1 (2024): 459. http://dx.doi.org/10.11591/ijeecs.v35.i1.pp459-466.

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Анотація:
The field of emotion research facilitates the development of several applications, all of which aim to precisely and swiftly identify emotions. Speech and facial expressions are the main focus of typical emotion analysis, although they are not accurate indicators of true feelings. Signal analysis, namely the electroencephalograph (EEG) of the brain signals, is the other area in which emotions are analyzed. When compared to other modalities, EEG offers precise and comprehensive data that facilitates the estimation of emotional states. In order to categories the emotions using an EEG signal, thi
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6

Deepthi, D. Kulkarni Vaibhav Vitthalrao Dixit Shweta Shirish Deshmukh. "Emotion detection using EEG: hybrid classification approach." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 1 (2024): 459–66. https://doi.org/10.11591/ijeecs.v35.i1.pp459-466.

Повний текст джерела
Анотація:
The field of emotion research facilitates the development of several applications, all of which aim to precisely and swiftly identify emotions. Speech and facial expressions are the main focus of typical emotion analysis, although they are not accurate indicators of true feelings. Signal analysis, namely the electroencephalograph (EEG) of the brain signals, is the other area in which emotions are analyzed. When compared to other modalities, EEG offers precise and comprehensive data that facilitates the estimation of emotional states. In order to categories the emotions using an EEG signal, thi
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7

Angreni, Ni Putu Dewi, Agus Muliantara, and Yuriko Christian. "Quantization-Based Novel Extraction Method Of EEG Signal For Classification." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 2 (2020): 169. http://dx.doi.org/10.24843/jlk.2020.v09.i02.p02.

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Анотація:
In the pattern recognition field, features or object’s characteristics are one of the key points to recognizing them. The feature extraction process will see that objects have different features, where the features are obtained through the analysis process from the extractor, such as for data statistics, energy, power spectral, and so on. This study aims to enrich the point of view of EEG signal features by quantifying the signal. It will be analyzed whether the features obtained by quantization represent the EEG signal object from different viewpoints. This research uses the DEAP dataset, wit
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8

Xu, Chang, Hong Liu, and Wei Qi. "EEG Emotion Recognition Based on Federated Learning Framework." Electronics 11, no. 20 (2022): 3316. http://dx.doi.org/10.3390/electronics11203316.

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Анотація:
Emotion recognition based on the multi-channel electroencephalograph (EEG) is becoming increasingly attractive. However, the lack of large datasets and privacy concerns lead to models that often do not have enough data for training, limiting the research and application of Deep Learn (DL) methods in this direction. At present, the popular federated learning (FL) approach, which can collaborate with different clients to perform distributed machine learning without sending data to a central server, provides a solution to the above problem. In this paper, we extended the FL method to the field of
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9

Bouazizi, Samar, Emna Benmohamed, and Hela Ltifi. "Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network." JUCS - Journal of Universal Computer Science 29, no. 10 (2023): 1116–38. http://dx.doi.org/10.3897/jucs.98789.

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Анотація:
Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion cla
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10

Bouazizi, Samar, Emna Benmohamed, and Hela Ltifi. "Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network." JUCS - Journal of Universal Computer Science 29, no. (10) (2023): 1116–38. https://doi.org/10.3897/jucs.98789.

Повний текст джерела
Анотація:
Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion cla
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Дисертації з теми "DEAP EEG"

1

Banville, Hubert. "Enabling real-world EEG applications with deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG005.

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Анотація:
Au cours des dernières décennies, les avancées révolutionnaires en neuroimagerie ont permis de considérablement améliorer notre compréhension du cerveau. Aujourd'hui, avec la disponibilité croissante des dispositifs personnels de neuroimagerie portables, tels que l'EEG mobile " à bas prix ", une nouvelle ère s’annonce où cette technologie n'est plus limitée aux laboratoires de recherche ou aux contextes cliniques. Les applications de l’EEG dans le " monde réel " présentent cependant leur lot de défis, de la rareté des données étiquetées à la qualité imprévisible des signaux et leur résolution
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2

Wasnik, Sachinkumar. "Fatigue Detection in EEG Time Series Data Using Deep Learning." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24917.

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Анотація:
Fatigue has widespread effects on the brain’s executive function, reaction time and information processing, causing loss of alertness, that affect safety, and productivity. There are various subjective and behavioural methods to measure fatigue. However, none of them is precise. The work in this thesis employs physiological measures such as heart rate, blood pressure, and breathing that are objective and quantitative indicators. These are thought to provide reliable measures of fatigue and may be easier to deploy in real world scenarios, compared to the subjective or behavioural methods. In p
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3

Brugattu, Valeria. "Deep learning con reti neurali convoluzionali per la classificazione di segnali EEG." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19978/.

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Анотація:
Il segnale elettroencefalografico (EEG) è un segnale complesso e la sua analisi ricopre un ruolo importante nell'ambito delle neuroscienze. Le tecniche di Deep Learning, grazie agli sviluppi degli ultimi anni, possono essere utilizzate per l’analisi e la classificazione del segnale EEG. Gli algoritmi di Deep Learning, basati sulle reti neurali multistrato, hanno dimostrato di avere la capacità di estrarre informazioni rilevanti dal segnale EEG e di facilitare il lavoro dell’analista, grazie all'estrazione automatica delle feature del segnale. In questo lavoro è stata studiata l’architettura d
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4

Sonkajärvi, E. (Eila). "The brain's electrical activity in deep anaesthesia:with special reference to EEG burst-suppression." Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526209722.

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Анотація:
Abstract Several anaesthetics are able to induce a burst-suppression (B-S) pattern in the electroencephalogram (EEG) during deep levels of anaesthesia. A burst-suppression pattern consists of alternating high amplitude bursts and periods of suppressed background activity. All monitors measuring the adequacy of anaesthesia recognize the EEG B-S as one criterion. A better understanding of EEG burst-suppression is important in understanding the mechanisms of anaesthesia. The aim of the study was to acquire a more comprehensive understanding of the function of neural pathways during deep anaesthes
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5

Pinnell, Richard Charles. "Miniature wireless deep-brain stimulator and EEG-recording device : implications for the treatment of schizophrenia." Thesis, University of Strathclyde, 2014. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=24074.

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Анотація:
Deep-brain stimulation (DBS) is increasingly being pursued as a treatment option for a range of neuropsychiatric disorders. When assessing its potential for the treatment of schizophrenia, related animal studies are often limited by the range of measurements that the equipment is capable of. In animals, the combination of DBS with various recording modalities such as local-field potential (LFP) recording has traditionally required complex and time-consuming laboratory setups. Furthermore, the elucidation of neural activity underpinning rodent behaviour has traditionally been hampered by the us
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6

Avilov, Oleksii. "Deep learning methods for motor imagery detection from raw EEG : applications to brain-computer interfaces." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0032.

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Анотація:
Cette thèse présente trois contributions pour améliorer la reconnaissance d’imaginations motrices utilisées par de nombreuses interfaces cerveau-ordinateur (BCI) comme moyen d'interaction. Tout d'abord, nous proposons d'estimer la qualité des images motrices en détectant des valeurs aberrantes et de les supprimer avant apprentissage. Ensuite, nous étudions la sélection des caractéristiques pour sept imaginations de mouvements. Enfin, nous présentons une architecture d'apprentissage profond reprenant les principes du réseaux EEGnet applicable directement sur des signaux électro-encéphalographiq
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7

Aslam, Adam Joshua. "EEG Characterization During Motor Tasks That Are Difficult for Movement Disorder Patients." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1822.

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Анотація:
Movement disorders are a group of syndromes that often arise due to neurological abnormalities. Approximately 40 million Americans are affected by some form of movement disorder, significantly impacting patients’ quality of life and their ability to live independently. Deep brain stimulation (DBS) is one treatment that has shown promising results in the past couple decades, however, the currently used open-loop system has several drawbacks. By implementing a closed-loop or adaptive DBS (aDBS) system, the need for expensive parameter reprogramming sessions would be reduced, side-effects may be
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8

Mariam, Tasnuva. "Burst-suppression events and fast, large-amplitude, sharp waves in the cortical EEG during deep isoflurane coma." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54331.

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9

Sors, Arnaud. "Apprentissage profond pour l'analyse de l'EEG continu." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAS006/document.

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Анотація:
Ces travaux de recherche visent à développer des méthodes d’apprentissage automatique pour l’analyse de l’électroencéphalogramme (EEG) continu. L’EEG continu est une modalité avantageuse pour l’évaluation fonctionnelle des états cérébraux en réanimation ou pour d’autres applications. Cependant son utilisation aujourd’hui demeure plus restreinte qu’elle ne pourrait l’être, car dans la plupart des cas l’interprétation est effectuée visuellement par des spécialistes.Les sous-parties de ce travail s’articulent autour de l’évaluation pronostique du coma post-anoxique, choisie comme application pilo
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10

Huang, Yan. "A Deep Learning Approach to Seizure Prediction with a Desirable Lead Time." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1548345388245127.

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Книги з теми "DEAP EEG"

1

Fields, Weston W. De Dode Zeerollen: Een korte geschiedenis. Brill, 2013.

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2

Liu, Xueqing. Fusing simultaneously acquired EEG-fMRI using deep learning. [publisher not identified], 2022.

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3

Dyreson, Patricia A. A very special egg. Port to Print], 1999.

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4

Maar, Birgitte de. A sea of houses: The residences from New Deal on Borneo/Sporenburg = Een zee van huizen : de woningen van New Dealp op Borneo/Sporenburg. THOTH, 1999.

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5

Hutorova, Natal'ya. Sustainable financing. Current trends and risks. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2180633.

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The monograph analyzes the main stages of the formation and development of the concept of sustainable development and the origins of the green economy. The directions of the integration of ESG principles into the sustainable development strategy are determined, approaches to assessing the progress of the integration of ESG approaches and ESG risks are analyzed. Special attention is paid to the development of socially responsible investment approaches and the formation of global sustainable finance markets. The main trends in global sustainable finance markets have been identified. It reflects
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6

Scialpi, Angela, and Alessio Mengoni, eds. La PCR e le sue varianti. Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-6453-159-5.

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The book "La PCR e le sue varianti" is designed as a reference tool for those whose laboratory activities deal with methods based on nucleic acid amplification. The text provides the theoretical bases of the polymerase chain reaction (PCR) and its variants (e.g. RT-PCR, quantitative PCR, isothermic PCR) in a rapid and concise manner and describes the principal applications used for genetic identification and the study of genetic polymorphism, in the form of a protocol that can be easily consulted by the users.
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7

(Firm), Drouot-Richelieu. Tableaux modernes XIXe et XXe siècles: Par Van der Voost, P. Wagner, A. Zarraga, L. Beroud, G. Clairin, E.G. Mac Avoy, F.L. Lanfant de Metz, S. Marchesi, Montergue, L. Deak Ebner, F. Maillaud, F.L. Français, R. Fouet, E. Galien-Laloue, J. Cabasson, C. Vernet, L. Seevagen, R. Fouet, L. Dappe, R. de Maynard ; Tableaux anciens des XVIIème et XVIIIème siècles : école française, école espagnole attribuée à Ramirez, école flamande autour de T. Van Aevanck ; Armes anciennes des XVIIIe et XIXe siècles : sabre d'officiers de chasseur à cheval d'infanterie, garde nationale, tambour major, sapeur, cavalerie anglaise, pistolet d'Arçon (XVIIIe siècle), tromblon, fusils d'infanterie, de grenadier, revolver type russian "Smith et Wesson" ; Sculptures de jardin XIXe et XXe siècles ; Objets d'art : opalines, argenterie, Théière argent US (1812-1850), pendule à la cathédrale, poupée bébé jumeau, flacon Lalique pour Molinard, verrerie 1930 par E. Gallée, d'Argental, Muller frères ; Mobilier XVIIIe, XIXe et XXe siècles : dont commode tombeau ép. Louis XV, commode ép. Régence, secrétaire ép. Louis XVI est. Dumoulin, commode ép. Louis XVI est. JB Hédouin, commode ép. Louis XVI est. Gallet, guéridon et secrétaire Louis Philippe, secrétaire Empire, chevet acajou époque XVIIIe, mobilier de style, table bouillotte, demi-lune, sièges. Gilles Néret-Minet, 2001.

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8

Egg Drop Dead. Cengage Gale, 2020.

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9

Egg drop dead. Berkley, 2016.

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10

Egg Drop Dead. Cengage Gale, 2016.

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Частини книг з теми "DEAP EEG"

1

Jha, Sonu Kumar, Somaraju Suvvari, and Mukesh Kumar. "Exploring the Impact of KNN and MLP Classifiers on Valence-Arousal Emotion Recognition Using EEG: An Analysis of DEAP Dataset and EEG Band Representations." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70906-7_1.

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2

Gao, Zhongke, and Xinmin Wang. "Deep Learning." In EEG Signal Processing and Feature Extraction. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_16.

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3

Acharya, Divya, Rehan Ahmed Sayyad, Priyanshu Dwivedi, Ashish Shaji, Panjala Sriram, and Arpit Bhardwaj. "EEG Signal Classification Using Deep Learning." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2709-5_30.

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4

Zhdanov, Maksim, Saskia Steinmann, and Nico Hoffmann. "Learning Generative Factors of EEG Data with Variational Auto-Encoders." In Deep Generative Models. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2_5.

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5

Kasabov, Nikola K. "Deep Learning and Deep Knowledge Representation of EEG Data." In Springer Series on Bio- and Neurosystems. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-57715-8_8.

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6

Nakagome, Sho, Alexander Craik, Akshay Sujatha Ravindran, Yongtian He, Jesus G. Cruz-Garza, and José L. Contreras-Vidal. "Deep Learning Methods for EEG Neural Classification." In Handbook of Neuroengineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-5540-1_78.

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Nakagome, Sho, Alexander Craik, Akshay Sujatha Ravindran, Yongtian He, Jesus G. Cruz-Garza, and Jose L. Contreras-Vidal. "Deep Learning Methods for EEG Neural Classification." In Handbook of Neuroengineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-15-2848-4_78-1.

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8

Radončić, Amina. "Deep Learning for Stress Detection – A 3D LSTM Model for EEG and ECG Data Analysis." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-95197-8_6.

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9

Liao, Chung-Yen, Rung-Ching Chen, and Qiao-En Liu. "Detecting Attention and Meditation EEG Utilized Deep Learning." In Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03748-2_25.

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Ripon, Shamim, Tashfia Choudhury, Shabrina Akter Shara, Sharmin Sharkar Rima, and Shaolin Jahan Aume. "Deep Learning Based Emotion Recognition Using EEG Signal." In Information Integration and Web Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48316-5_22.

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Тези доповідей конференцій з теми "DEAP EEG"

1

Zou, Jiadao, and Qingxue Zhang. "Deep-Source EEG Generation with Deep Learning from Different Configurations of Surface EEG." In 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10929772.

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2

Zeng, Qingyu, Shuai Zhang, Lei Sun, and Xiuqing Mao. "EEG-Based Identification Using Conformer Networks." In 2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2025. https://doi.org/10.1109/cvidl65390.2025.11085857.

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3

Kumar, Jayasheel, Deepak Kumar Soni, Ginni Nijhawan, Krishna Chythanya Nagaraju, Tamam Ali Abd Ulabbas Abedi, and P. Subhashini. "Hybrid Machine Learning and Deep Learning Models for Mental State Classification Using EEG and ECG Signals." In 2025 International Conference on Intelligent Control, Computing and Communications (IC3). IEEE, 2025. https://doi.org/10.1109/ic363308.2025.10956870.

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Kumar, Lokesh, Kumar Kaustubh, Tonmoy Rajkhowa, and Shashaank Aswatha Mattur. "EEG-ViLSTM: A Deep Learning Approach for Depression Detection Using EEG Signals." In 2025 National Conference on Communications (NCC). IEEE, 2025. https://doi.org/10.1109/ncc63735.2025.10983346.

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5

Lin, Songlu, Zhihong Wang, Yuzhe Wang, and Jie Liu. "CS-EEG: A Deep Compressed Sensing Approach for Single-Channel EEG Reconstruction." In 2024 3rd International Conference on Health Big Data and Intelligent Healthcare (ICHIH). IEEE, 2024. https://doi.org/10.1109/ichih63459.2024.11064872.

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6

Jin, Hulin, Zhiran Jin, Yong-Guk Kim, and Kun Li. "A Novel Emotion Recognition System Based on Non-Invasive EEG Signals." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10932987.

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7

K, Indraneel, and Trinath Basu Miriyala. "Artificial Intelligence Based Emotion Recognition from Fuzzy EEG Signals: A Comprehensive Review." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933235.

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8

Gabriel-Alexandru, Vrabie, and Lascu Mihaela-Ruxandra. "Deep Learning for Sleep Staging Using EEG Signals." In 2024 International Symposium on Electronics and Telecommunications (ISETC). IEEE, 2024. https://doi.org/10.1109/isetc63109.2024.10797312.

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9

Geetha, R., and D. Jenila Rani. "Deep Forest based EEG Signal Analysis and Classification." In 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2024. https://doi.org/10.1109/iceca63461.2024.10801115.

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Meghana, Alokam, Bilakanti Vanshika, Karnam VedaSamhitha, Keerthana Murali, Rimjhim Padam Singh, and Suja Palaniswamy. "EEG-Based Emotion Recognition Using Deep Learning Models." In 2024 2nd International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC). IEEE, 2024. https://doi.org/10.1109/icmacc62921.2024.10894376.

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Звіти організацій з теми "DEAP EEG"

1

Brown, Donald W., and Robert H. Hendron. Experiment 2066. Stimulation of the deep part of EE-3A. Office of Scientific and Technical Information (OSTI), 1986. http://dx.doi.org/10.2172/1244389.

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2

Wan Brown, Jackie, Katherine Rogers, and Alys Young. What is the evidence underpinning clinical assessment of mental health of deaf adults with learning disabilities: A scoping review protocol. INPLASY - INPLASY International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.6.0007.

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Анотація:
Review question / Objective: This scoping review aims to explore the literature to identify the types, scope and quality of evidence underpinning clinical assessment of deaf adults with learning disabilities, with or without autism, with mental health issues and/or challenging behaviour. Deaf adults are included in the review regardless of their communication modality, e.g. signed or spoken language, or other methods. The results of this review will inform considerations for future research and inform clinical practice. As an experiential type review, the PICO framework (Richardson et al. 1995
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3

Kong, Q. Deep Learning Based Approach to Integrate MyShake's Trigger Data with ShakeAlert for Faster and Robust EEW Alerts. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1836932.

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4

EFFC/DFI Concrete Task Group. German Guide to Tremie Concrete for Deep Foundations, 2nd Edition - Leitfaden Kontraktorbetonfür Tiefgründungen, Zweite Fassung. European Federation of Foundation Contractors and Deep Foundations Institute, 2018. https://doi.org/10.37308/effc-dfi-ctg-trem-germe2-2018.

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The primary purpose of this report is to provide guidance for characterization related to performance, mix design process, and methods used to test fresh concrete. The principles of this guide apply to tremie concrete for deep foundations but may also be applied for other forms of deep foundations (e.g., continuous flight auger piling). The guide addresses design considerations including concrete rheology, mix design, reinforcement detailing, concrete cover, and good practice rules for placement. A review of methods to test the as-built elements is presented together with advice on the identif
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5

Rogers, Katherine, Karina Lovell, Peter Bower, and Christopher Armitage. “What are Deaf sign language users’ experiences as patients in healthcare services?”: A scoping review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.1.0102.

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Review question / Objective: A scoping review with specific reference to the context of Deaf populations, in relation to Deaf people’s experience of health and mental health services, including the use of a questionnaire regarding their experience as a patient, is needed in order to assess and synthesise the current knowledge. As this is an exploratory type of review drawing on qualitative as well as quantitative work, the PICo approach Population, (Phenomena of) Interest and Context, will guide the question formulation. Following the identification of the gap in the existing systematic review
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6

EFFC/DFI Concrete Task Group. Guide to Tremie Concrete for Deep Foundations, 3rd Edition. European Federation of Foundation Contractors and Deep Foundations Institute, 2024. https://doi.org/10.37308/effc-dfi-ctg-trem-e3-2024.

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Анотація:
The primary purpose of this Guide is to give guidance on fresh concrete characterisation with respect to its performance, the concrete mix design process, and the methods used to test the fresh concrete. The principles of this Guide apply to tremie concrete for deep foundations but may also be applied for other forms of deep foundations (e.g. continuous flight auger piling). This Third Edition of the Tremie Guide includes a general review of the Second Edition, comprises more specific advice on testing fresh concrete (in a completely revised Section 5.3), emphasises the Task Group’s understand
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7

Swannack, Robyn, Alys Young, and Claudine Storbeck. A scoping review of deaf sign language users’ perceptions and experiences of well-being in South Africa. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.11.0082.

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Анотація:
Background: This scoping review concerns deaf adult sign language users from any country (e.g. users of South African Sign Language (SASL), British Sign Language (BSL), American Sign Language (ASL) and so forth). It concerns well-being understood to include subjective well-being and following the WHO’s (2001) definition of well-being as “mental health as a state of well-being in which every individual realises his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully and is able to make a contribution to his or her community.” Well-being has thre
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8

Hellwig, Runa T., Despoina Teli, Marcel Schweiker, et al. Guidelines for low energy building design based on the adaptive thermal comfort concept - Technical report: IEA EBC Annex 69: Strategy and Practice of Adaptive Thermal Comfort in Low Energy Buildings. Aalborg University, 2022. http://dx.doi.org/10.54337/aau510903564.

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The adaptive thermal comfort concept has been developed over many years and proven in numerous field studies (e.g. Webb 1964, Nicol and Humphreys 1973, Auliciems 1981b, de Dear et al. 1997, McCartney and Nicol 2002, Manu et al. 2016), showing that people are satisfied with a wide range of thermal conditions. Prerequisite is that people are provided with means to make themselves comfortable, that they know which opportunities they have, that it is socially acceptable to use these opportunities and that they are willing to use them (Hellwig, 2015). However, the overall understanding of how to de
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9

Malej, Matt, and Fengyan Shi. Suppressing the pressure-source instability in modeling deep-draft vessels with low under-keel clearance in FUNWAVE-TVD. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40639.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) documents the development through verification and validation of three instability-suppressing mechanisms in FUNWAVE-TVD, a Boussinesq-type numerical wave model, when modeling deep-draft vessels with a low under-keel clearance (UKC). Many large commercial ports and channels (e.g., Houston Ship Channel, Galveston, US Army Corps of Engineers [USACE]) are traveled and affected by tens of thousands of commercial vessel passages per year. In a series of recent projects undertaken for the Galveston District (USACE), it was discovered tha
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10

García Ferro, Luz Ángela, Elba N. Luna, Lorena Rodríguez, Micha Van Waesberghe, and Darinka Vásquez Jordán. Peer Assist. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0009034.

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Анотація:
This document describes the Peer Assist, which is a facilitated workshop, held face-to-face or virtually, in which a diverse group of participants from inside and/or outside the Bank share their experiences and insights with a team that wants to benefit from what others have learned, before it decides on a specific plan or course of action to deal with a significant upcoming challenge. Different from more informal peer to peer learning opportunities (e.g. networking, mentoring, Q&A, BBLs), the Peer Assist is a structured process designed to tackle challenges of greater complexity, uncertai
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