Figure 2. Procedure for generating synthetic EGG time series. Image is adopted from the preprint (Miljković et al., 2023) and shared under the CC BY 4.0 DEED. iFFT stands for inverse Fast Fourier Transform. Research Activities in the Biosignal Measurement & Analysis Research Group Ilija Tanasković1,2, Nenad B. Popović1, Đorđe D. Nešković1,3, Smilja Stokanović1, Mijat Paunović1, Nadica Miljković1,4 1University of Belgrade – School of Electrical Engineering, Belgrade, Serbia 2Institute for Artificial Intelligence R&D, Serbia 3Vinca Institute of Nuclear Sciences - National Institute of the Republic of Serbia, University of Belgrade 4Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia About BMA Research Group The Biosignal Measurement & Analysis (BMA) group is an informal research group at the University of Belgrade – School of Electrical Engineering (ETF). The group consists of four PhD students and one alumnus led by Associate Professor Nadica Miljković. Main research focus of the BMA group from ETF is in biomedical and clinical engineering, signal and image processing, as well as in application of Machine Learning (ML). Specifically, the BMA research is related to the measurement and analysis of electromyograms (EMG), electrogastrograms (EGG), electrocardiograms (ECG), impedance cardiograms (ICG), electrodermal activity (EDA), eye tracker data, and other signals in healthy subjects and in patients. This poster highlights selected research topics from the BMA group performed in cooperation with other institutions. Peak Detection and Biometric Identification Novel peak detection algorithm tailored for enhancing the extraction of information of interest in noisy biomedical signals has been developed. The algorithm can be applied on abdominal electrocardiogram (ECG) and eye tracker data (Tanasković & Miljković, 2023; Tanasković et al., 2023a). In the case of abdominal ECG, proposed approach allows fetal heart rate monitoring during the pregnancy, which is the key indicator of prenatal well-being. Furthermore, we combined our knowledge in signal processing and ML to create a biometric identification system using ECG and impedance cardiogram (ICG) data. Our research also digs into the influence of varying emotional states during the interaction with the system, paving the way for further research into human-computer interaction. Nonlinear Dynamics in Heart Rhythm Аnalysis Tools used in the analysis of nonlinear dynamic systems (such as Poincaré plots) are developed and adapted with the aim to uncover the complex mechanisms that govern heart rate variability. Integrating these techniques into routine clinical practice and wearable technology may lead to improvement of patients' monitoring and health outcomes. (Paunović et al., 2024) Figure 1. The comparative analysis of four ML models performance when emotional state of individual differs during the enrolment and evaluation phase. Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (GNB). The image is modification of image taken from (Tanasković et al., 2023b) and the image is distributed under Creative Commons Attribution License CC-BY. Feature Extraction and ML for Topographic Maps Currently, we are focusing on the analysis of topographic pressure maps obtained from the matrix-arranged pressure sensors to classify lying postures. The study is oriented towards a thorough examination of the most relevant features and on the most appropriate hyperparameters of machine learning algorithms for successful recognition of lying postures (supine, left lateral, and right lateral subjects' positions). (Nešković & Miljković, 2023) Figure 5. Image shows a Poincaré plot that represents the heart rate dynamics of a sample subject with normal sinus rhythm, sourced from the PhysioNet Normal Sinus Rhythm RR Interval Database. ML for Missing Data Imputation The BMA group research delves into the impact of missing data o