SIPPRE

Assistant Professor @ECE UoP

PhD Success at SIPPRE: Advancing AI-Driven Breast Cancer Detection

/ / PhD Theses

The SIPPRE Research Group (Signal, Image Processing and Pattern Recognition Group) proudly announces the successful defense of the PhD thesis of Dr. Dionysios Anyfantis, entitled:

“Study and Development of an Automated System for the Detection and Recognition of Suspicious Cancerous Regions in Mammography.”

This achievement represents a major milestone for both the researcher and SIPPRE, further reinforcing the group’s strong research footprint in artificial intelligence for medical imaging and healthcare innovation.

Research Impact & Innovation

Dr. Anyfantis’ doctoral research delivers a comprehensive, end-to-end framework for automated breast cancer detection, addressing one of the most critical challenges in modern diagnostic radiology. The work combines advanced image processing, machine learning, and pattern recognition techniques to identify and classify suspicious regions in digital mammograms with high reliability.

Key contributions of the thesis include:

  • Sophisticated mammographic image enhancement and segmentation methods
  • Intelligent feature extraction and classification pipelines
  • Computer-Aided Diagnosis (CAD) systems designed to support radiologists in early and accurate cancer detection

The proposed system demonstrates how AI-driven tools can enhance diagnostic confidence, reduce interpretation variability, and contribute to improved patient outcomes.

Strengthening SIPPRE’s Research Vision

This PhD thesis is a flagship example of SIPPRE’s ongoing commitment to:

  • Cutting-edge medical image analysis
  • Explainable and trustworthy AI
  • Translational research bridging engineering and clinical practice

The results of this work enrich SIPPRE’s research portfolio and open new avenues for future collaborations in biomedical imaging, clinical AI systems, and digital health technologies.

Congratulations

The SIPPRE Research Group extends its warmest congratulations to Dr. Dionysios Anyfantis on this outstanding accomplishment and wishes him continued success in his scientific and professional journey.

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Diploma Thesis Update at SIPPRE (Robotics)

/ / Diploma Theses, Experiments

EEG-Based Motor Imagery BCI for Robotic Arm Control

The SIPPRE Research Group (Signal, Image Processing and Pattern Recognition Group) is pleased to share a progress update from the ongoing diploma thesis of Panagiotis Leventogiannis, entitled:

“EEG-Based Motor Imagery Brain–Computer Interface (BCI) for Robotic Arm Control.”

Current Progress

As demonstrated in the latest experimental video, the robotic hand successfully performs controlled finger closing, validating the mechanical design and actuation pipeline of the system.
The robotic arm and hand are fully 3D-printed in the SIPPRE Lab using the Creality K1C printer, featuring a tendon-driven mechanism optimized for smooth and precise motion.

Research Scope

The thesis aims to develop an end-to-end BCI system that translates EEG-based motor imagery into real-time control commands for a robotic arm. The work brings together:

  • Robotics & mechatronics (3D-printed arm/hand, actuation)
  • Brain–Computer Interfaces (BCI)
  • EEG signal acquisition and processing
  • Machine learning for motor imagery decoding

What’s Next

The next phase of the project will focus on:

  • EEG recordings from human subjects
  • Training and evaluation of motor imagery classifiers
  • Closing the loop between brain signals and robotic control

📣 Call for Volunteers Coming Soon
Announcements for EEG recording sessions and volunteer participation will follow shortly. Stay tuned to the SIPPRE website and social channels for updates.

This project reflects SIPPRE’s ongoing research efforts in BCI systems, neuroengineering, and human–machine interaction, bridging brain signal analysis with real-world robotic applications.

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Call for Participants – Game Experiments on Dynamic Difficulty Adjustment (DDA)

/ / Diploma Theses, Experiments

The SIPPRE Research Group (Signal, Image Processing and Pattern Recognition Group) invites students to participate in an experimental study on Dynamic Difficulty Adjustment (DDA) in digital games.

The study is conducted in the context of the undergraduate diploma thesis of Georgios Vasilikopoulos, a student of the Department of Electrical and Computer Engineering, University of the Peloponnese, entitled:

“Dynamic Difficulty Adjustment (DDA) in Games”
(Supervisor: Associate Professor Athanasios Koutras)

Research Objective

The aim of this study is to collect gameplay data from real users through a custom-developed game, in order to:

  • investigate real-time difficulty adaptation mechanisms,
  • analyze player–system interaction,
  • and contribute to research on adaptive and player-centered game systems.

Participation Details

  • Duration: approximately 20 minutes
  • Mode: in-person participation
  • Location: Building K, Office K1.03
  • Anonymity: All collected data are anonymous and confidential, and will be used exclusively for research purposes
  • Questionnaire: After completing the session, participants will be asked to fill in a short online questionnaire

📅 Register to Participate

Students interested in participating are kindly requested to reserve a time slot by selecting a convenient date and time via the following link:

👉 https://calendly.com/basoph2002/dynamic-difficulty-adjustment-in-games-experiments

Your participation will make a valuable contribution to research on intelligent and adaptive game design.

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Diploma Thesis Defense | Maria Karadimitropoulou

/ / Thesis Presentations

Diploma Thesis Success: AI & EEG Analysis for Neurodegenerative Disease Detection

The SIPPRE Research Group (Signal, Image Processing and Pattern Recognition Group) is pleased to announce the successful completion and public defense of the Diploma Thesis of Maria Karadimitropoulou, entitled:

“EEG Analysis for the Discrimination between Healthy Subjects and Patients with Alzheimer’s Disease and Frontotemporal Dementia: Machine Learning Techniques and Brain Connectivity Analysis.”

Research Focus & Contribution

The thesis addresses a critical challenge in modern neuroscience and biomedical engineering: the early and reliable differentiation of neurodegenerative diseases using non-invasive brain signals.

The work focuses on the processing and analysis of electroencephalographic (EEG) recordings from:

  • Patients with Alzheimer’s Disease (AD)
  • Patients with Frontotemporal Dementia (FTD)
  • Healthy control subjects (CN)

Using advanced signal processing, machine learning techniques, and brain connectivity analysis, the study develops automated pipelines capable of distinguishing between these groups and extracting meaningful neurophysiological biomarkers associated with each condition.

Alignment with SIPPRE Research

This diploma thesis is fully aligned with SIPPRE’s research directions in:

  • EEG and brain signal analysis
  • Machine learning for biomedical applications
  • Functional brain connectivity and network analysis
  • Data-driven approaches for neurological disease characterization

The work contributes to SIPPRE’s broader vision of AI-enabled, interpretable, and clinically relevant neurotechnology.

Congratulations to Maria Karadimitropoulou on this excellent academic achievement, and best wishes for her continued academic and professional path.

Diploma Thesis Defense | Maria Karadimitropoulou Read More »

Erasmus+ Staff Training Visit at SIPPRE

/ / Collaborations, Lab News

The SIPPRE Research Group (Signal, Image Processing and Pattern Recognition Group) had the pleasure of hosting Dr. Rukiye Orman and Dr. Abdullah Orman from Ankara Yıldırım Beyazıt University, Turkey, in the framework of Erasmus+ Staff Mobility for Training.

The visit took place from 29 September to 3 October 2025 at the Department of Electrical and Computer Engineering, University of the Peloponnese, and was coordinated by Dr. Athanasios Koutras, Associate Professor and Erasmus+ Departmental Coordinator.

Training Activities & Scientific Exchange

During their stay, the visiting colleagues participated in a structured training and knowledge-exchange programme that included:

  • A comprehensive visit to the SIPPRE Laboratory, with presentations of ongoing research in
    machine learning, artificial intelligence, biomedical signal analysis, and data-driven technologies
  • Technical discussions and demonstrations related to brain–computer interfaces, EEG analysis, digital image processing, and AI-based diagnostic systems
  • Meetings with academic staff and researchers, focusing on modern teaching methodologies and research-driven education
  • Visits to additional teaching and research laboratories of the department, covering embedded systems, robotics, digital communications, and computing platforms
  • The programme enabled hands-on exposure to current research infrastructures and fostered meaningful scientific dialogue and exchange of best practices.

Impact & Future Collaboration

This Erasmus+ mobility contributed significantly to:

  • strengthening academic and cultural ties between the two institutions,
  • supporting the internationalisation strategies of both universities,
  • and laying the groundwork for future collaborations, including joint research activities, student mobility, and academic exchanges.

The SIPPRE Research Group remains committed to promoting international cooperation, open research culture, and knowledge sharing through Erasmus+ and other mobility initiatives.

We sincerely thank Dr. Rukiye Orman and Dr. Abdullah Orman for their visit and look forward to continued collaboration.

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Diploma Thesis Opportunity: Brain-Computer Interface Smartwatch Integration

/ / Diploma Theses, Equipment

The SIPPRE Research Group is pleased to announce a new diploma thesis position focusing on the integration of Brain-Computer Interfaces (BCI) with consumer-grade smartwatches.

Thesis Overview

The project aims to develop a real-time brain signal monitoring system that streams processed EEG data from BCI devices to consumer smartwatches. The system will provide immediate biometric feedback, such as frequency band power visualization (Alpha, Beta, Theta), through an intuitive wearable interface.

Hardware Platforms

  • BCI Devices: EmotiBit, OpenBCI Cyton/Ganglion, or Enophone

  • Smartwatches: Google Pixel Watch 2 or Samsung Galaxy Watch7 (provided)

System Design

A hybrid architecture will be explored, involving:

  • Signal acquisition via BCI hardware

  • Real-time processing (filtering, FFT, band power extraction) using the Brainflow framework

  • Data distribution through an Android application

  • Wear OS visualization on a smartwatch with custom UI for live feedback

An alternative, mobile-only implementation will also be considered to eliminate the PC dependency.

Research Goals

  • Demonstrate feasibility of BCI-smartwatch integration

  • Evaluate user experience and interface design for biometric feedback

  • Benchmark system latency and reliability

  • Explore applications in meditation, sleep analysis, focus training, and cognitive load assessment.

Expected Outcomes

  • A fully functional prototype system (open-source)

  • Benchmarks on performance and latency

  • User interface design guidelines for wearable biometric displays

  • Academic contributions on the use of consumer wearables in BCI research

Innovation Potential

This project bridges professional-grade BCI technology with everyday consumer wearables, paving the way for more accessible neurotechnology in wellness and cognitive training applications.

Diploma Thesis Opportunity: Brain-Computer Interface Smartwatch Integration Read More »

Welcome Our New PhD Student: Niki Michalopoulou

/ / Lab News, PhD Theses

The SIPPRE Research Group is delighted to welcome Niki Michalopoulou as a new PhD candidate at the Department of Electrical & Computer Engineering, University of Peloponnese.

Her doctoral research, supervised by Assoc. Prof. Athanasios Koutras, will focus on EEG-Based Neurofeedback and AI for Learning and Memory.

Niki’s work will explore how electroencephalography (EEG) can be combined with neurofeedback and advanced artificial intelligence techniques to enhance cognitive processes such as learning and memory. The project aims to develop intelligent closed-loop systems that personalize training, adapt in real-time to brain activity, and open new possibilities for cognitive enhancement in healthy individuals.

This research lies at the intersection of neuroscience, AI, and brain–computer interfacing, and aligns with SIPPRE’s mission to push the boundaries of signal processing and pattern recognition in biomedical applications.

We are excited to have Niki join our team and look forward to the innovative contributions she will bring to our research community.

Welcome Our New PhD Student: Niki Michalopoulou Read More »

Welcome to Our Visiting Student – Teodora Cvijović

/ / Diploma Theses, Lab News

We are delighted to welcome Teodora Cvijović, a Master’s student from the University of Novi Sad (Serbia), who joins the SIPPRE Research Group as a visiting student.

Teodora will conduct her Diploma Thesis under the co-supervision of Assoc. Prof. Athanasios Koutras, focusing on “Advances in Automated EEG Analysis for Sleep Spindles, K-Complex Recognition, and Sleep Staging”

Her research will:

  • Provide a comprehensive review of state-of-the-art methodologies for automated sleep analysis, covering sleep spindles, K-complexes, and sleep staging.

  • Examine modern machine learning and deep learning approaches, including CNNs, RNNs, and Transformers, applied to EEG recordings.

  • Explore key datasets (Sleep-EDF, MASS, ISRUC-Sleep, SHHS, DREAMS, etc.) and their role in benchmarking automated systems.

  • Discuss the clinical and research implications of automated detection for sleep disorders, cognitive neuroscience, and neurodegenerative diseases.

  • Identify current challenges and future directions, with emphasis on explainable AI (XAI), multi-modal integration, and real-world clinical translation.

Through this work, Teodora will gain expertise in biomedical signal processing, literature synthesis, and critical analysis of cutting-edge algorithms, while contributing valuable insights into the fast-evolving field of automated sleep EEG analysis.

We warmly welcome Teodora to SIPPRE and look forward to her contributions during her research stay!

Welcome to Our Visiting Student – Teodora Cvijović Read More »

Welcome to Our New Member – Athanasia Braimi

/ / Diploma Theses

We are pleased to welcome Athanasia Braimi, a final-year student at the Department of Electrical & Computer Engineering, University of Peloponnese, as a new member of the SIPPRE Research Group.

Athanasia will conduct her Diploma Thesis under the supervision of Assoc. Prof. Athanasios Koutras on the topic:

“Hypnopsis: A Synopsis of Sleep Quality through Automated EEG Staging and Visual Analytics.”

Her research will focus on the development of an integrated system for sleep quality analysis using EEG signals. The project includes:

  • Applying deep learning models for automated sleep staging (N1, N2, N3, REM).

  • Extracting key sleep biomarkers, such as spindles and slow-wave activity.

  • Designing a visual analytics dashboard (hypnogram, spectral power, quality metrics) for exploratory analysis of sleep structure.

  • Investigating the relationship between automatically recognized sleep stages and quantitative indicators of sleep quality.

Through this work, Athanasia will gain expertise in biomedical signal processing, deep learning for time-series data, and interactive data visualization while contributing to our ongoing research in neurotechnology and health applications.

We warmly welcome Athanasia to SIPPRE and look forward to her contributions in advancing our research on sleep analysis and brain signal processing.

Welcome to Our New Member – Athanasia Braimi Read More »

Diploma Thesis Opportunity: Multimodal Emotion and Gameplay Analysis with PlayStation 4

/ / Diploma Theses, Equipment

The SIPPRE Research Group invites applications for a Diploma Thesis in the exciting field of multimodal analysis of player experience in gaming environments.

The project aims to design, implement, and evaluate a comprehensive experimental platform that records and analyzes the physiological and emotional responses of volunteers while playing on the PlayStation 4. Two contrasting genres will be studied:

  • 🧩 Brain/cognitive games that require focus and strategic thinking.

  • ⚡ Action games that emphasize speed, reflexes, and intense engagement.

By comparing these categories, the thesis will investigate how different gameplay conditions affect the brain, the body, and the emotions of players.

Multimodal Data Sources

The student will integrate and synchronize diverse signals, including:

  • 🎮 Gameplay Recording via Elgato Cam Link.

  • 🧠 EEG (OpenBCI) to monitor brain activity.

  • ❤️ Physiological Signals (EmotiBit) – heart rate, GSR, SpO₂.

  • 👀 Eye Tracking to measure focus and attention shifts.

  • 😊 Facial Emotion Recognition from a webcam.

  • 🎛️ Controller Interaction Logging using a custom Arduino or Raspberry Pi device to capture button presses in real time.

Student Role & Responsibilities

The candidate will:

  • Set up the full experimental environment and ensure precise synchronization of all devices.

  • Recruit and manage volunteers for controlled gaming experiments.

  • Implement data fusion across modalities (EEG, physiology, gaze, face, gameplay events).

  • Conduct emotion recognition and correlation analysis across different game types.

Applications & Research Impact

This work directly contributes to our ongoing SIPPRE research on Dynamic Difficulty Adjustment (DDA), where game difficulty adapts to the player’s state. Insights from this thesis could enable:

  • 🎮 Adaptive Gaming Systems that adjust difficulty in real time based on stress, engagement, or fatigue.

  • 🧠 Cognitive Workload Monitoring for training or educational games.

  • ⚡ eSports Analytics to study performance under pressure.

  • 🩺 Mental Health and Stress Research using games as experimental environments.

What the Student Will Gain

  • Experience with state-of-the-art multimodal recording and synchronization (EEG, EmotiBit, eye tracking, video capture).

  • Skills in signal processing, machine learning, and human–computer interaction.

  • Training in experimental design and data collection with human participants.

  • A project at the intersection of gaming, neuroscience, and AI, with potential for scientific publications.

This is a unique opportunity for a motivated student to combine gaming, biosignals, and adaptive AI to shape the future of interactive systems.

📩 Interested candidates should contact Assoc. Prof. Athanasios Koutras for further details.

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