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Chhade F, Tabbal J, Paban V, Auffret M, Hassan M, Vérin M. Predicting creative behavior using resting-state electroencephalography. Commun Biol 2024; 7:790. [PMID: 38951602 PMCID: PMC11217288 DOI: 10.1038/s42003-024-06461-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 06/14/2024] [Indexed: 07/03/2024] Open
Abstract
Neuroscience research has shown that specific brain patterns can relate to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to creative behavior using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity in novel subjects. We acquired resting state HD-EEG data from 90 healthy participants who completed a creative behavior inventory. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results reveal functional connectivity patterns related to high and low creativity, in the gamma frequency band (30-45 Hz). In leave-one-out cross-validation, the combined model of high and low networks predicts individual creativity with very good accuracy (r = 0.36, p = 0.00045). Furthermore, the model's predictive power is established through external validation on an independent dataset (N = 41), showing a statistically significant correlation between observed and predicted creativity scores (r = 0.35, p = 0.02). These findings reveal large-scale networks that could predict creative behavior at rest, providing a crucial foundation for developing HD-EEG-network-based markers of creativity.
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Affiliation(s)
- Fatima Chhade
- CIC-IT INSERM 1414, Université de Rennes, Rennes, France.
| | - Judie Tabbal
- Institute of Clinical Neurosciences of Rennes (INCR), Rennes, France
- MINDIG, Rennes, France
| | - Véronique Paban
- CRPN, CNRS-UMR 7077, Aix Marseille Université, Marseille, France
| | - Manon Auffret
- CIC-IT INSERM 1414, Université de Rennes, Rennes, France
- France Développement Électronique, Monswiller, France
| | - Mahmoud Hassan
- MINDIG, Rennes, France
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
| | - Marc Vérin
- CIC-IT INSERM 1414, Université de Rennes, Rennes, France
- B-CLINE, Laboratoire Interdisciplinaire pour l'Innovation et la Recherche en Santé d'Orléans (LI²RSO), Université d'Orléans, Orléans, France
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2
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Chen Y, Fazli S, Wallraven C. An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior. Sci Data 2024; 11:389. [PMID: 38627400 PMCID: PMC11021485 DOI: 10.1038/s41597-024-03234-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/05/2024] [Indexed: 04/19/2024] Open
Abstract
Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods' efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.
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Affiliation(s)
- Yiyu Chen
- Department of Artificial Intelligence, Korea University, Seoul, 02841, South Korea
| | - Siamac Fazli
- Department of Computer Science, Nazarbayev University, Astana, 010000, Kazakhstan
| | - Christian Wallraven
- Department of Artificial Intelligence, Korea University, Seoul, 02841, South Korea.
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, South Korea.
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Lin S, Jiang J, Huang K, Li L, He X, Du P, Wu Y, Liu J, Li X, Huang Z, Zhou Z, Yu Y, Gao J, Lei M, Wu H. Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces. ACS NANO 2023; 17:24487-24513. [PMID: 38064282 DOI: 10.1021/acsnano.3c06781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.
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Affiliation(s)
- Sen Lin
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jingjing Jiang
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Kai Huang
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Lei Li
- National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
| | - Xian He
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Peng Du
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Yufeng Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Junchen Liu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xilin Li
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Advanced Institute for Brain and Intelligence, Guangxi University, Nanning 530004, China
| | - Zhibao Huang
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Zenan Zhou
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Yuanhang Yu
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jiaxin Gao
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Ming Lei
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Hui Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
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Eqlimi E, Bockstael A, Schönwiesner M, Talsma D, Botteldooren D. Time course of EEG complexity reflects attentional engagement during listening to speech in noise. Eur J Neurosci 2023; 58:4043-4069. [PMID: 37814423 DOI: 10.1111/ejn.16159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 08/31/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023]
Abstract
Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.
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Affiliation(s)
- Ehsan Eqlimi
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
| | - Annelies Bockstael
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
| | | | - Durk Talsma
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Dick Botteldooren
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
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Gu Y, Gagnon JF, Kaminska M. Sleep electroencephalography biomarkers of cognition in obstructive sleep apnea. J Sleep Res 2023; 32:e13831. [PMID: 36941194 DOI: 10.1111/jsr.13831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/20/2022] [Accepted: 01/04/2023] [Indexed: 03/23/2023]
Abstract
Obstructive sleep apnea has been associated with cognitive impairment and may be linked to disorders of cognitive function. These associations may be a result of intermittent hypoxaemia, sleep fragmentation and changes in sleep microstructure in obstructive sleep apnea. Current clinical metrics of obstructive sleep apnea, such as the apnea-hypopnea index, are poor predictors of cognitive outcomes in obstructive sleep apnea. Sleep microstructure features, which can be identified on sleep electroencephalography of traditional overnight polysomnography, are increasingly being characterized in obstructive sleep apnea and may better predict cognitive outcomes. Here, we summarize the literature on several major sleep electroencephalography features (slow-wave activity, sleep spindles, K-complexes, cyclic alternating patterns, rapid eye movement sleep quantitative electroencephalography, odds ratio product) identified in obstructive sleep apnea. We will review the associations between these sleep electroencephalography features and cognition in obstructive sleep apnea, and examine how treatment of obstructive sleep apnea affects these associations. Lastly, evolving technologies in sleep electroencephalography analyses will also be discussed (e.g. high-density electroencephalography, machine learning) as potential predictors of cognitive function in obstructive sleep apnea.
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Affiliation(s)
- Yusing Gu
- Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jean-François Gagnon
- Department of Psychology, Université du Québec à Montréal, Montréal, Québec, Canada
- Center for Advanced Research in Sleep Medicine, CIUSSS-NÎM - Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Marta Kaminska
- Respiratory Epidemiology and Clinical Research Unit, Research Institute of the McGill University Health Centre, Montreal, Québec, Canada
- Respiratory Division & Sleep Laboratory, McGill University Health Centre, Montreal, Québec, Canada
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A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking. Sci Data 2022; 9:416. [PMID: 35842416 PMCID: PMC9288455 DOI: 10.1038/s41597-022-01538-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 07/11/2022] [Indexed: 11/26/2022] Open
Abstract
We present a dataset combining high-density Electroencephalography (HD-EEG, 128-channels) and mouse-tracking intended as a resource for examining the dynamic decision process of semantics and preference choices in the human brain. The dataset includes resting-state and task-related (food preference choices and semantic judgments) EEG acquired from 31 individuals (ages: 18–33). Along with the dataset, we also provided the preliminary microstate analysis of resting-state EEG and the ERPs, topomap, and time-frequency maps of the task-related EEG. We believe that the simultaneous mouse-tracking and EEG recording would crack the core components of binary choices and further index the temporal dynamics of decision making and response hesitation. This publicly available dataset could support the development of neural signal processing methods in motor EEG, thus advancing research in both the decision neuroscience and brain-computer interface (BCI) applications. Measurement(s) | brain measurement (resting/task) • mouse trajectory; binary decision-making | Technology Type(s) | high-density EEG • Experiment |
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Kabbara A, Robert G, Khalil M, Verin M, Benquet P, Hassan M. An electroencephalography connectome predictive model of major depressive disorder severity. Sci Rep 2022; 12:6816. [PMID: 35473962 PMCID: PMC9042869 DOI: 10.1038/s41598-022-10949-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 04/05/2022] [Indexed: 11/21/2022] Open
Abstract
Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.
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Affiliation(s)
- Aya Kabbara
- Lebanese Association for Scientific Research, Tripoli, Lebanon
- MINDig, F-35000, Rennes, France
| | - Gabriel Robert
- Academic Department of Psychiatry, Centre Hospitalier Guillaume Régnier, Rennes, France
- Empenn, U1228, IRISA, UMR 6074, Rennes, France
- Comportement et Noyaux Gris Centraux, EA 4712, CHU Rennes, Université de Rennes 1, 35000, Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon
- CRSI Research Center, Faculty of Engineering, Lebanese University, Beirut, Lebanon
| | - Marc Verin
- Comportement et Noyaux Gris Centraux, EA 4712, CHU Rennes, Université de Rennes 1, 35000, Rennes, France
- Univ Rennes, Inserm, LTSI-U1099, F-35000, Rennes, France
| | - Pascal Benquet
- Univ Rennes, Inserm, LTSI-U1099, F-35000, Rennes, France
| | - Mahmoud Hassan
- MINDig, F-35000, Rennes, France.
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland.
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