https://www.um.edu.mt/library/oar/handle/123456789/520 2026-04-28T00:18:13Z 2026-04-28T00:18:13Z https://www.um.edu.mt/library/oar/handle/123456789/145952 2026-04-27T12:55:32Z 2025-10-01T00:00:00Z

Title: Annual report - 2024-2025 Abstract: This report isthe 17th issue of teh Activity Report of the Department of Systems and Control Engineering covering academic year 2024/25. This report formally records and communicates various activities and capabilities of the Department staff members to students, the University, International academic partners, industrial collaborators and the geberal public.

2025-10-01T00:00:00Z Türk, Stefanie Padfield, Natasha Mujahid, Kamran Camilleri, Tracey A. Camilleri, Kenneth P. https://www.um.edu.mt/library/oar/handle/123456789/144577 2026-03-04T09:21:06Z 2025-01-01T00:00:00Z

Title: Word-specific properties affect classification performance in brain computer interfaces for decoding imagined speech from EEG Authors: Türk, Stefanie; Padfield, Natasha; Mujahid, Kamran; Camilleri, Tracey A.; Camilleri, Kenneth P. Abstract: Decoding imagined speech from brain signals has become one of the most significant fields for BCI applications. One of the current challenges that researchers face is an insufficient classification performance for real-world applications. In this study, we investigate for the first time the effect of word-specific properties known to modulate brain signals on classification performance. We chose 16 word prompts that vary in age of acquisition (AoA) and word frequency, two word-specific properties known to modulate speech processing, and investigated their classification performance for speech imagery (SI) trials compared to the idle state using a random forest classifier and 10-fold cross-validation. We found highly significant effects of AoA, word frequency and their interaction on classification performance. Our results yield evidence that the word frequency and AoA of word prompts used in SI paradigms significantly influence the classification accuracy in a BCI application when SI trials are compared to the idle state.

2025-01-01T00:00:00Z Gauci, Jean Falzon, Owen Camilleri, Kenneth P. https://www.um.edu.mt/library/oar/handle/123456789/144560 2026-03-04T06:39:19Z 2025-01-01T00:00:00Z

Title: Registration of long-term recordings of thermographic video applied to foot temperature monitoring Authors: Gauci, Jean; Falzon, Owen; Camilleri, Kenneth P. Abstract: Dynamic thermal imaging of human subjects presents unique challenges to automated data processing. Variations in background-foreground contrast and diverse patterns on regions of interest of the body mean that classical processing techniques which were developed for RGB images might not be suitable for this kind of data. Additionally, subject movement during recording complicates the process further and necessitates correction for accurate thermal video analysis. In this study, a method for registering thermal video data is presented, allowing each pixel to correspond to the same anatomical location throughout the video. This registration facilitates subsequent processing, such as ROI extraction. The proposed registration method has two steps: the first addresses large linear deformations, while the second uses deep learning based on the SynthMorph architecture to register smaller, elastic deformations. This method manages to reduce the mean displacement of salient points by 71.5% on our test dataset. The algorithm was tested on thermal video data of the plantar aspect of human feet but has the potential to be implemented on other greyscale images and in other medical applications.

2025-01-01T00:00:00Z Mifsud, Matthew Camilleri, Tracey A. Camilleri, Kenneth P. https://www.um.edu.mt/library/oar/handle/123456789/144557 2026-03-03T15:06:50Z 2025-01-01T00:00:00Z

Title: Real-time EOG signal baseline drift estimation using passive VOG data Authors: Mifsud, Matthew; Camilleri, Tracey A.; Camilleri, Kenneth P. Abstract: One of the main challenges when it comes to electrooculography (EOG)-based eye gaze tracking for the control of human-computer interface systems is the drifting baseline. This slow wander in the signal leads to erroneous gaze angle estimates and over time, can make operating an application impossible. Baseline component estimation techniques have been proposed in the literature in order to model and remove the baseline drift component, however, most of these can only be carried out in an offline manner. In this work, we propose a novel drift mitigation technique which may be used to de-drift EOG signals in real-time without requiring users to fixate at known target locations. The proposed approach makes use of a low-sampling rate passive videooculography (VOG) source to model and remove the EOG signal baseline whilst preserving the signal’s original morphology. It’s performance, in terms of the horizontal and vertical gaze angle estimation error is evaluated against standard baseline estimation techniques using data from ten subjects, demonstrating improved performance.

2025-01-01T00:00:00Z