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Eyvazpour R, Navi FFT, Shakeri E, Nikzad B, Heysieattalab S. Machine learning-based classifying of risk-takers and risk-aversive individuals using resting-state EEG data: A pilot feasibility study. Brain Behav 2023; 13:e3139. [PMID: 37366037 PMCID: PMC10498077 DOI: 10.1002/brb3.3139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/29/2023] [Accepted: 06/15/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Decision-making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers' personality traits (i.e., risk-taking or risk-averse) in recent years. Although there are findings of signal decision-making and brain activity, the implementation of an intelligent brain-based technique to predict risk-averse and risk-taking managers is still in doubt. METHODS This study proposes an electroencephalogram (EEG)-based intelligent system to distinguish risk-taking managers from risk-averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time-frequency domain analysis method, was used on resting-state EEG data to extract statistical features. Then, a two-step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features. RESULTS Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1-measure, indicating that machine learning (ML) models can distinguish between risk-taking and risk-averse managers using the features extracted from the alpha frequency band in 10 s analysis window size. CONCLUSIONS The findings of this study demonstrate the potential of using intelligent (ML-based) systems in distinguish between risk-taking and risk-averse managers using biological signals.
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Affiliation(s)
- Reza Eyvazpour
- Department of Biomedical Engineering, School of Electrical EngineeringIran University of Science and Technology (IUST)TehranIran
| | | | - Elmira Shakeri
- Department of Business Management, Faculty of Management and AccountingAllameh Tabataba'i UniversityTehranIran
| | - Behzad Nikzad
- Department of Cognitive NeuroscienceUniversity of TabrizTabrizIran
- Neurobioscince DivisionResearch Center of Bioscience and Biotechnology, University of TabrizTabrizIran
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Zemla K, Sedek G, Wróbel K, Postepski F, Wojcik GM. Investigating the Impact of Guided Imagery on Stress, Brain Functions, and Attention: A Randomized Trial. SENSORS (BASEL, SWITZERLAND) 2023; 23:6210. [PMID: 37448060 PMCID: PMC10346678 DOI: 10.3390/s23136210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023]
Abstract
The aim of this study was to investigate the potential impact of guided imagery (GI) on attentional control and cognitive performance and to explore the relationship between guided imagery, stress reduction, alpha brainwave activity, and attentional control using common cognitive performance tests. Executive function was assessed through the use of attentional control tests, including the anti-saccade, Stroop, and Go/No-go tasks. Participants underwent a guided imagery session while their brainwave activity was measured, followed by attentional control tests. The study's outcomes provide fresh insights into the influence of guided imagery on brain wave activity, particularly in terms of attentional control. The findings suggest that guided imagery has the potential to enhance attentional control by augmenting the alpha power and reducing stress levels. Given the limited existing research on the specific impact of guided imagery on attention control, the study's findings carry notable significance.
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Affiliation(s)
- Katarzyna Zemla
- Institute of Psychology, SWPS University of Social Sciences and Humanities, 03-815 Warsaw, Poland; (K.Z.)
| | - Grzegorz Sedek
- Institute of Psychology, SWPS University of Social Sciences and Humanities, 03-815 Warsaw, Poland; (K.Z.)
| | - Krzysztof Wróbel
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, 20-033 Lublin, Poland (F.P.)
| | - Filip Postepski
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, 20-033 Lublin, Poland (F.P.)
| | - Grzegorz M. Wojcik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, 20-033 Lublin, Poland (F.P.)
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3
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Wojcik GM, Shriki O, Kwasniewicz L, Kawiak A, Ben-Horin Y, Furman S, Wróbel K, Bartosik B, Panas E. Investigating brain cortical activity in patients with post-COVID-19 brain fog. Front Neurosci 2023; 17:1019778. [PMID: 36845422 PMCID: PMC9947499 DOI: 10.3389/fnins.2023.1019778] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/12/2023] [Indexed: 02/11/2023] Open
Abstract
Brain fog is a kind of mental problem, similar to chronic fatigue syndrome, and appears about 3 months after the infection with COVID-19 and lasts up to 9 months. The maximum magnitude of the third wave of COVID-19 in Poland was in April 2021. The research referred here aimed at carrying out the investigation comprising the electrophysiological analysis of the patients who suffered from COVID-19 and had symptoms of brain fog (sub-cohort A), suffered from COVID-19 and did not have symptoms of brain fog (sub-cohort B), and the control group that had no COVID-19 and no symptoms (sub-cohort C). The aim of this article was to examine whether there are differences in the brain cortical activity of these three sub-cohorts and, if possible differentiate and classify them using the machine-learning tools. he dense array electroencephalographic amplifier with 256 electrodes was used for recordings. The event-related potentials were chosen as we expected to find the differences in the patients' responses to three different mental tasks arranged in the experiments commonly known in experimental psychology: face recognition, digit span, and task switching. These potentials were plotted for all three patients' sub-cohorts and all three experiments. The cross-correlation method was used to find differences, and, in fact, such differences manifested themselves in the shape of event-related potentials on the cognitive electrodes. The discussion of such differences will be presented; however, an explanation of such differences would require the recruitment of a much larger cohort. In the classification problem, the avalanche analysis for feature extractions from the resting state signal and linear discriminant analysis for classification were used. The differences between sub-cohorts in such signals were expected to be found. Machine-learning tools were used, as finding the differences with eyes seemed impossible. Indeed, the A&B vs. C, B&C vs. A, A vs. B, A vs. C, and B vs. C classification tasks were performed, and the efficiency of around 60-70% was achieved. In future, probably there will be pandemics again due to the imbalance in the natural environment, resulting in the decreasing number of species, temperature increase, and climate change-generated migrations. The research can help to predict brain fog after the COVID-19 recovery and prepare the patients for better convalescence. Shortening the time of brain fog recovery will be beneficial not only for the patients but also for social conditions.
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Affiliation(s)
- Grzegorz M. Wojcik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland,*Correspondence: Grzegorz M. Wojcik ✉
| | - Oren Shriki
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel,Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Lukasz Kwasniewicz
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Andrzej Kawiak
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Yarden Ben-Horin
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Sagi Furman
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Krzysztof Wróbel
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Bernadetta Bartosik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Ewelina Panas
- Department of International Relations, Faculty of Political Science and Journalism, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
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Ding Y, Chu Y, Liu M, Ling Z, Wang S, Li X, Li Y. Fully automated discrimination of Alzheimer's disease using resting-state electroencephalography signals. Quant Imaging Med Surg 2022; 12:1063-1078. [PMID: 35111605 DOI: 10.21037/qims-21-430] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/24/2021] [Indexed: 12/29/2022]
Abstract
Background The Alzheimer's disease (AD) population increases worldwide, placing a heavy burden on the economy and society. Presently, there is no cure for AD. Developing a convenient method of screening for AD and mild cognitive impairment (MCI) could enable early intervention, thus slowing down the progress of the disease and enabling better overall disease management. Methods In the current study, resting-state electroencephalography (EEG) data were acquired from 113 normal cognition (NC) subjects, 116 amnestic MCI patients, and 72 probable AD patients. After preprocessing by an automatic algorithm, features including spectral power, complexity, and functional connectivity were extracted, and machine-learning classifiers were built to differentiate among the 3 groups. The classification performance was evaluated from multiple perspectives, including accuracy, specificity, sensitivity, area under the curve (AUC) with 95% confidence intervals, and compared to the empirical chance level by permutation tests. Results The analysis of variance results (P<0.05 with false discovery rate correction) confirmed the tendency to slow brain activity, reduced complexity, and connectivity with AD progress. By combining the features, the ability of the machine-learning classifiers, especially the ensemble trees, to differentiate among the 3 groups, was significantly better than that of the empirical chance level of the permutation test. The AUC of the classifier with the best performance was 80.08% for AD vs. NC, 70.82% for AD vs. MCI, and 63.95% for MCI vs. NC. Conclusions The current study presented a fully automatic procedure that could significantly distinguish NC, MCI, and AD subjects via resting-state EEG signals. The study was based on a large data set with evidence-based medical diagnosis and provided further evidence that resting-state EEG data could assist in the discrimination of AD patients.
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Affiliation(s)
- Yue Ding
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China
| | - Yinxue Chu
- iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China
| | - Meng Liu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zhenhua Ling
- National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China
| | - Shijin Wang
- iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China.,State Key Laboratory of Cognitive Intelligence, Hefei, China
| | - Xin Li
- iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China.,National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China
| | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
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5
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Tao Q, Si Y, Li F, Li P, Li Y, Zhang S, Wan F, Yao D, Xu P. Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG. Int J Neural Syst 2021; 31:2150031. [PMID: 34167448 DOI: 10.1142/s0129065721500313] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.
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Affiliation(s)
- Qin Tao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yajing Si
- School of Psychology, Xinxiang Medical University, Hena, 453000, P. R. China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Peiyang Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P. R. China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Shu Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Feng Wan
- Faculty of Science and Technology, University of Macau, 999078, Macau
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
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Toma FM, Miyakoshi M. Left Frontal EEG Power Responds to Stock Price Changes in a Simulated Asset Bubble Market. Brain Sci 2021; 11:brainsci11060670. [PMID: 34063778 PMCID: PMC8223788 DOI: 10.3390/brainsci11060670] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/17/2021] [Accepted: 05/19/2021] [Indexed: 11/16/2022] Open
Abstract
Financial bubbles are a result of aggregate irrational behavior and cannot be explained by standard economic pricing theory. Research in neuroeconomics can improve our understanding of their causes. We conducted an experiment in which 28 healthy subjects traded in a simulated market bubble, while scalp EEG was recorded using a low-cost, BCI-friendly desktop device with 14 electrodes. Independent component (IC) analysis was performed to decompose brain signals and the obtained scalp topography was used to cluster the ICs. We computed single-trial time-frequency power relative to the onset of stock price display and estimated the correlation between EEG power and stock price across trials using a general linear model. We found that delta band (1-4 Hz) EEG power within the left frontal region negatively correlated with the trial-by-trial stock prices including the financial bubble. We interpreted the result as stimulus-preceding negativity (SPN) occurring as a dis-inhibition of the resting state network. We conclude that the combination between the desktop-BCI-friendly EEG, the simulated financial bubble and advanced signal processing and statistical approaches could successfully identify the neural correlate of the financial bubble. We add to the neuroeconomics literature a complementary EEG neurometric as a bubble predictor, which can further be explored in future decision-making experiments.
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Affiliation(s)
| | - Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0559, USA
- Correspondence:
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Quantitative EEG measures in profoundly deaf and normal hearing individuals while performing a vibrotactile temporal discrimination task. Int J Psychophysiol 2021; 166:71-82. [PMID: 34023377 DOI: 10.1016/j.ijpsycho.2021.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/10/2021] [Accepted: 05/16/2021] [Indexed: 11/22/2022]
Abstract
Challenges in early oral language acquisition in profoundly deaf individuals have an impact on cognitive neurodevelopment. This has led to the exploration of alternative sound perception methods involving training of vibrotactile discrimination of sounds within the language spectrum. In particular, stimulus duration plays an important role in linguistic categorical perception. We comparatively evaluated vibrotactile temporal discrimination of sound and how specific training can modify the underlying electrical brain activity. Fifteen profoundly deaf (PD) and 15 normal-hearing (NH) subjects performed a vibrotactile oddball task with simultaneous EEG recording, before and after a short training period (5 one-hour sessions; in 2.5-3 weeks). The stimuli consisted of 700 Hz pure-tones with different duration (target: long 500 ms; non-target: short 250 ms). The sound-wave stimuli were delivered by a small device worn on the right index finger. A similar behavioral training effect was observed in both groups showing significant improvement in sound-duration discrimination. However, quantitative EEG measurements reveal distinct neurophysiological patterns characterized by higher and more diffuse delta band magnitudes in the PD group, together with a generalized decrement in absolute power in both groups that might reflect a facilitating process associated to learning. Furthermore, training-related changes were found in the beta-band in NH. Findings suggest PD have different cognitive adaptive mechanisms which are not a mere amplification effect due to greater cortical excitability.
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Zapała D, Mikołajewski D. Computational model of decreased suppression of mu rhythms in patients with Autism Spectrum Disorders during movement observation—preliminary findings. BIO-ALGORITHMS AND MED-SYSTEMS 2021. [DOI: 10.1515/bams-2020-0064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Objectives
Autism Spectrum Disorders (ASD) represent developmental conditions with deficits in the cognitive, motor, communication and social domains. It is thought that imitative behaviour may be impaired in children with ASD. The Mirror Neural System (MNS) concept plays an important role in theories explaining the link between action perception, imitation and social decision-making in ASD.
Methods
In this study, Emergent 7.0.1 software was used to build a computational model of the phenomenon of MNS influence on motion imitation. Seven point populations of Hodgkin–Huxley artificial neurons were used to create a simplified model.
Results
The model shows pathologically altered processing in the neural network, which may reflect processes observed in ASD due to reduced stimulus attenuation. The model is considered preliminary—further research should test for a minimally significant difference between the states: normal processing and pathological processing.
Conclusions
The study shows that even a simple computational model can provide insight into the mechanisms underlying the phenomena observed in experimental studies, including in children with ASD.
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Affiliation(s)
- Dariusz Zapała
- Department of Experimental Psychology , The John Paul II Catholic University of Lublin , Lublin , Poland
| | - Dariusz Mikołajewski
- Institute of Computer Science, Kazimierz Wielki University , Bydgoszcz , Poland
- Neurocognitive Laboratory, Interdisciplinary Center for Modern Technologies, Nicolaus Copernicus University , Toruń , Poland
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Kawiak A, Wojcik GM, Schneider P, Kwasniewicz L, Wierzbicki A. Whom to Believe? Understanding and Modeling Brain Activity in Source Credibility Evaluation. Front Neuroinform 2021; 14:607853. [PMID: 33381019 PMCID: PMC7768004 DOI: 10.3389/fninf.2020.607853] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 11/23/2020] [Indexed: 12/04/2022] Open
Abstract
Understanding how humans evaluate credibility is an important scientific question in the era of fake news. Source credibility is among the most important aspects of credibility evaluations. One of the most direct ways to understand source credibility is to use measurements of brain activity of humans performing credibility evaluations. Nevertheless, source credibility has never been investigated using such a method before. This article reports the results of an experiment during which we have measured brain activity during source credibility evaluation, using EEG. The experiment allowed for identification of brain areas that were active when a participant made positive or negative source credibility evaluations. Based on experimental data, we modeled and predicted human source credibility evaluations using EEG brain activity measurements with F1 score exceeding 0.7 (using 10-fold cross-validation).
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Affiliation(s)
- Andrzej Kawiak
- Chair of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
| | - Grzegorz M Wojcik
- Chair of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
| | - Piotr Schneider
- Chair of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
| | - Lukasz Kwasniewicz
- Chair of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
| | - Adam Wierzbicki
- Polish-Japanese Academy of Information Technology, Warsaw, Poland
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Duch W, Mikołajewski D. Modelling effects of consciousness disorders in brainstem computational model – Preliminary findings. BIO-ALGORITHMS AND MED-SYSTEMS 2020. [DOI: 10.1515/bams-2020-0018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Objectives
Disorders of consciousness are very big medical and social problem. Their variability, problems in precise definition and proper diagnosis make difficult assessing their causes and effectiveness of the therapy. In the paper we present our point of view to a problem of consciousness and its most common disorders.
Methods
For this moment scientists do not know exactly, if these disorders can be a result of simple but general mechanism, or a complex set of mechanisms, both on neural, molecular or system level. Presented in the paper simulations using neural network models, including biologically relevant consciousness’ modelling, help assess influence of specified causes.
Results
Nonmotoric brain activity can play important role within diagnostic process as a supplementary method for motor capabilities. Simple brain sensory (e.g. visual) processing of both healthy subject and people with consciousness disorders help checking hypotheses in the area of consciousness’ disorders’ mechanisms, including associations between consciousness and its neural correlates.
Conclusions
The results are promising. Project announced herein will be developed and its next result will be presented in subsequent articles.
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Affiliation(s)
- Włodzisław Duch
- Department of Informatics , Nicolaus Copernicus University , Toruń , Poland
- Neurocognitive Laboratory, Center for Modern Interdisciplinary Technologies , Nicolaus Copernicus University , Toruń , Poland
| | - Dariusz Mikołajewski
- Department of Informatics , Nicolaus Copernicus University , Toruń , Poland
- Neurocognitive Laboratory, Center for Modern Interdisciplinary Technologies , Nicolaus Copernicus University , Toruń , Poland
- Institute of Informatics , Kazimierz Wielki University , Bydgoszcz , Poland
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Krzhizhanovskaya VV, Závodszky G, Lees MH, Dongarra JJ, Sloot PMA, Brissos S, Teixeira J. Look Who’s Talking: Modeling Decision Making Based on Source Credibility. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7302235 DOI: 10.1007/978-3-030-50371-0_24] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Understanding how humans evaluate credibility is an important scientific question in the era of fake news. Source credibility is among the most important aspects of credibility evaluations. One of the most direct ways to understand source credibility is to use measurements of brain activity of humans who make credibility evaluations. Nevertheless, source credibility has never been investigated using such a method before. This article reports the results of an experiment during which we have measured brain activity during source credibility evaluation using EEG. The experiment allowed for identification of brain areas that were active when a participant made positive or negative source credibility evaluations. Based on experimental data, we modelled and predicted human source credibility evaluations using EEG brain activity measurements with F1 score exceeding 0.7 (using 10-fold cross-validation).
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How You Say or What You Say? Neural Activity in Message Credibility Evaluation. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7302259 DOI: 10.1007/978-3-030-50371-0_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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