SIPPRE

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.

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.

The SIPPRE Research Group is excited to announce an open position for a Diploma Thesis in the cutting-edge area of Virtual Reality (VR) and Brain-Computer Interfaces (BCI).

Using our newly acquired Meta Quest 3S headset, the selected student will develop an innovative VR application that interacts with brain (EEG) or muscle (EMG) signals. The specific direction (EEG or EMG) will be defined as the project evolves.

What we are looking for

• No prior experience is required – motivation and willingness to learn are the most important assets.

• Familiarity with Unity or game development is a plus, but not mandatory.

• Strong commitment, creativity, and enthusiasm for research in VR, neuroscience, and human-computer interaction.

What you will gain

• Hands-on experience with VR development and biosignal-based interfaces.

• Training and support from the SIPPRE team in signal processing, machine learning, and interactive system design.

• Opportunity to work with state-of-the-art equipment (Meta Quest 3S, EEG/EMG systems).

• A chance to contribute to emerging research in BCI and VR applications, opening pathways for publications and future research.

If you are curious, motivated, and ready to dive into the world where technology meets the human mind and body, this thesis is for you!

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