Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Smart Cities: Challenges and Opportunities
Mohammad Ilyas
(pages: 1-6)

Bridging the Gap: Communicating to Increase the Visibility and Impact of Your Academic Work
Erin Ryan
(pages: 7-12)

Cross-Cultural Online Networking Based on Biomedical Engineering to Motivate Transdisciplinary Communication Skills
Shigehiro Hashimoto
(pages: 13-17)

Interdisciplinary Approaches to Learning Informatics
Masaaki Kunigami
(pages: 18-22)

The Impact of Artificial Intelligence and the Importance of Transdisciplinary Research
R. Cherinka, J. Prezzama, P. O'Leary
(pages: 23-28)

Emotional Communication as Complex Phenomenon in Musical Interpretation – Proposal for a Systemic Model That Promotes a Transdisciplinary Process of Self-Formation and Reflection Around Expressiveness as a Lived Experience
Fuensanta Fernández de Velazco, Eduardo Carpinteyro-Lara, Saúl Rodríguez-Luna
(pages: 29-33)

A Multi-Disciplinary Cybernetic Approach to Pedagogic Excellence
Russell Jay Hendel
(pages: 34-41)

The Ethics of Artificial Intelligence in the Era of Generative AI
Vassilka D. Kirova, Cyril S. Ku, Joseph R. Laracy, Thomas J. Marlowe
(pages: 42-50)

Trans-Disciplinary Communication: Context and Semantics
Maurício Vieira Kritz
(pages: 51-57)

A Brave New World: AI as a Nascent Regime?
Jasmin Cowin, Birgit Oberer, Cristo Leon
(pages: 58-66)

The Role of Art and Science – Relational Dynamics in Human Ecology
Giorgio Pizziolo, Rita Micarelli
(pages: 67-75)

Advancing Entrepreneurship Education: An Integrated Approach to Empowering Future Innovators
Birgit Oberer, Alptekin Erkollar
(pages: 76-81)

Harmonizing Horizons: The Symphony of Human-Machine Collaboration in the Age of AI
Birgit Oberer, Alptekin Erkollar
(pages: 82-86)

How Do Students Learn Artificial Intelligence in Interdisciplinary Field of Biomedical Engineering?
Shigehiro Hashimoto
(pages: 87-91)

What is ChatGPT and its Present and Future for Artificial Intelligence in Trans-Disciplinary Communications?
Richard Segall
(pages: 92-98)


 

Abstracts

 


ABSTRACT


Coupling Functions between Brain Waves: Significance of Opened/Closed Eyes

Lal Hussain, Wajid Aziz, Sharjil Saeed


In dynamical systems, the information flows converge or diverges in state space and is integrated or communicated between different cells assemblies termed as CFC. This process allows different oscillatory systems to communicate in accurate time, control and distribute the information flows in cell assemblies. The CF interactions allow the oscillatory rhythms to communicate in accurate time, and reintegrate the separated information. The intrinsic brain dynamics in Electroencephalography (EEG) with eye - closed (EC) and eye open (EO) during resting states have been investigated to see the changes in brain complexity i.e. simple visual processing which are associated with increase in global dimension complexity. In order to study these changes in EEG, we have computed the coupling to see the inhibitory interneurons response and inter-regions functional connectivity differences between the eye conditions. We have investigated the fluctuations in EEG activities in low (delta, theta) and high (alpha) frequency brain oscillations. Coupling strength was estimated using Dynamic Bayesian inference approach which can effectively detect the phase connectivity subject to the noise within a network of time varying coupled phase oscillators. Using this approach, we have seen that delta-alpha and theta-alpha CFC are more dominant in resting state EEG and applicable to multivariate network oscillator. It shows that alpha phase was dominated by low frequency oscillations i.e. delta and theta. These different CFC help us to investigate complex neuronal brain dynamics at large scale networks. We observed the local interactions at high frequencies and global interactions at low frequencies. The alpha oscillations are generated from both posterior and anterior origins whereas the delta oscillations found at posterior regions.

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