Month: September 2023

New Article: Electrochemical Biosensor Design Through Data-Driven Modeling Incorporating Meta-Analysis and Big Data Workflow

New Article: Electrochemical Biosensor Design Through Data-Driven Modeling Incorporating Meta-Analysis and Big Data Workflow

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We are pleased to informed that the team from University of Bilesko-Biala published a paper presenting workflow enabling us to execute both empirical and analytical studies of enzyme kinetics. For this purpose, on the one hand, we are based on a series of experimental research involving the traditional methods and techniques used when studying biochemical reactions and designing electrochemical biosensors: conductance research, spectroscopy, and electromagnetic field study.

Full paper can be found on Electrochemical Biosensor Design Through Data-Driven Modeling Incorporating Meta-Analysis and Big Data Workflow | SpringerLink

Panayotova, Galina,Dimitrov, Georgi,Dimitrov, Willian,Petrov, Pavel,Petrova, Pepa,Tsvetkova, Paulina, Quantitative Analysis of EEG Signal Profiles, 2023, 2023 13th International Conference on Advanced Computer Information Technologies, ACIT 2023 – Proceedings Pages 627 – 6302023 13th International Conference on Advanced Computer Information Technologies, ACIT 2023Wroclaw 21 September 2023through 23 September 2023 Code 193540; ISBN – 979-8350311; DOI – 10.1109/ICEST58410.2023.10187310;

Panayotova, Galina,Dimitrov, Georgi,Dimitrov, Willian,Petrov, Pavel,Petrova, Pepa,Tsvetkova, Paulina, Quantitative Analysis of EEG Signal Profiles, 2023, 2023 13th International Conference on Advanced Computer Information Technologies, ACIT 2023 – Proceedings Pages 627 – 6302023 13th International Conference on Advanced Computer Information Technologies, ACIT 2023Wroclaw 21 September 2023through 23 September 2023 Code 193540; ISBN – 979-8350311; DOI – 10.1109/ICEST58410.2023.10187310;

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The use of data derived from Brain Computer Interaction (BCI) is a complex process that requires multidisciplinary skills and knowledge in computer science, signal processing, neuroscience, robotics, artificial intelligence, and other related fields. In this study, we propose the quantitative analysis method for feature selection based on descriptive statistics (maximum, minimum, mean and median).The purpose of this paper is to show that different trials of the same individual at the same symbol yield similar brain profiles regardless of their mental state, and that different individuals have different brain profiles at the same visual signals.

The obtained data are discussed in the context of studying the nature of brain signals.