BIA 2021 (to be held September 16 - 19, 2021) is organized and sponsored by Technical University of Varna and technically supported by IEEE and IEEE Bulgaria CAS/SSC Joint Chapter.

Indexing: Accepted papers will be submitted for inclusion into IEEE Xplore . All conference papers included in IEEE Xplore will be indexed in the SCOPUS database.

Important dates: 

• Full Paper Submission: June 1, 2021
• Acceptance Notification: July 1, 2021
• Final paper submission: July 30, 2021
• Registration of Accepted papers by TBA

Paper submissions: We invite authors to submit original work of up to 4 pages. All submissions should be in English language. Each submission will be reviewed by at least two reviewers. Each author can appear in more than one paper. 

The submission of papers is handled through the Easy Chair conference management system.

Paper Templates: All submissions must follow the MS Word or LaTeX IEEE manuscript templates provided by IEEE eXpress Conference Publishing:

Download Manuscript Templates

Ethical Requirements: 

• Each submission should present original work on a BIA-related topic.
• Plagiarism check will be performed before inclusion in the IEEE Xplore Digital Library.
• Each paper have to be presented by one of the authors.

Conference Chairman 

Prof. Todor Ganchev - Technical University of Varna, Bulgaria

Technical Co-Chair

Assoc. Prof. Valentina Markova - Technical University of Varna, Bulgaria

Organizing Committee:

Prof. Vencislav Valchev - Technical University of Varna, Bulgaria

Prof. Karl Gudmundsson - University of Iceland - Háskóli Íslands, Iceland

Dr. Otilia Kocsis - University of Patras, Greece

Prof. Dagan Feng - University of Sydney, Australia

Assoc. Prof. Rossitza Goleva - IEEE Section Bulgaria

Assoc. Prof. Kiril Alexiev - IEEE Section Bulgaria

Assoc. Prof. Iosif Mporas - University of Hertfordshire, UK

Dr. Sonya Vachinska - Technical University of Varna, Bulgaria

Eng. Kalin Kalinkov - Technical University of Varna, Bulgaria

Eng. Prolet Deneva - Technical University of Varna, Bulgaria

Program Committee: 

Prof. Ivan Ganchev - University of Limerick, Ireland; University of Plovdiv “Paisii Hilendarski”, BULGARIA 

Prof. Evelina Pencheva – Technical University of Sofia, Bulgaria

Prof. Ivaylo Atanasov – Technical University of Sofia, Bulgaria

Assoc. Prof. Valentina Markova - Technical University of Varna, Bulgaria

Assoc. Prof. Rossitza Goleva - IEEE Section Bulgaria

Assoc. Prof. Kiril Alexiev - IEEE Section Bulgaria

Assoc. Prof. Iosif Mporas - University of Hertfordshire, UK

Dr. Theodoros Kostoulas - Bournemouth University, UK

Dr. Otilia Kocsis - University of Patras, Greece

Dr. Firgan Feradov - Technical University of Varna, Bulgaria

Agenda

*The detailed technical programme will become available by the end of August 2020

The CLAS (Cognitive Load, Affect and Stress) dataset was conceived as a freely accessible repository which is purposely developed to support research on the automated assessment of certain states of mind and the emotional condition of a person. This resource is intended to support RTD activities aiming at the development of intelligent human computer interaction (HCI) interfaces that incorporate functionalities allowing for the automated recognition of human emotions, the automated detection of stress conditions, the automated assessment of the degree of concentration, cognitive load, and momentary cognitive capacity, and can account for some personality traits related to the ability to quickly solve logical and mathematical problems under strict time constraints. These and other related functionalities open additional opportunities in support of the advancement of research on (i) intelligent human-machine interfaces in entertainment, e-Health, e-Learning, e-Government and other applications; (ii) improving the efficiency of human-robot collaboration systems; (iii) risk assessment and management systems in robotics and in high-risk professions (rescue crews, military, miners etc.); and (iv) a wide range of personal applications aiming at the enhancement of health-related quality of life and wellbeing.

The CLAS dataset is a resource of labeled physiological recordings, which contain synchronized ECG, PPG,  EDA and acclerometer signals captured while the test subjects are involved in some purposely designed interactive or perceptive task. Specifically, the interactive tasks are designed to evaluate different aspects of the momentary cognitive load, the degree of attention and concentration, or the cognitive capacity of a person. Specifically, in the interactive tasks the test subject is presented with a set of purposely designed mathematical and logical problems with the strict requirement for prompt response within a short time window.In the perceptive tasks, the test subjects are exposed to a series of carefully selected stimuli which aim to elicit emotions. In the perceptive tasks the test subjects typically receive instructions to simply watch the images or to follow the video clips. More details here.

Although the particular focus of our research is on the states of mind associated with negative emotions, mental strain and high cognitive effort, the CLAS dataset could offer an adequate support to research of a wider scope, such as general studies on attention assessment, cognitive load assessment, emotion recognition, as well as stress detection.

When using the CLAS dataset, please cite:

Markova, V., Ganchev, T., Kalinkov, K. (2019). CLAS: A Database for Cognitive Load, Affect and Stress Recognition, in Proceedings of the International Conference on Biomedical Innovations and Applications, (BIA-2019), art. no. 8967457,  DOI: 10.1109/BIA48344.2019.8967457. Available on-line: https://ieeexplore.ieee.org/document/8967457

You can freely access a sample (PPG, ECG, EDA, Accelerometer) from here.

For receiving access to the whole CLAS dataset, please follow the procedure bellow:

1. To receive the password for the dataset, please sign the EULA , scan it and send it back via this form or via email available at the Contact page, if the form is not accessible at the time of signing.
2. We will send you the password within three working days
3. Download the CLAS Dataset¹ (password protected).

_{1Compressed size - 2.03 GB, Uncompressed size - 13.9 GB,  we recommend using 7-zip for extraction.}