Computational neurosdynamics
We study how the brain evolves over time using continuous EEG signals during both spontaneous states, such as mind-wandering and resting-state, and structured, continuous tasks, including speech tracking, music listening, and naturalistic cognition.
We develop and apply computational methods to track neural dynamics, uncovering how information is processed, maintained, and integrated over time. By combining signal processing, time-series analysis, and modeling techniques, we aim to reveal the temporal structure of brain activity and its relation to behavior and perception.
Our work emphasizes continuous, naturalistic paradigms rather than discrete trials, providing insight into the brain’s ongoing computations in real-world-like contexts.
Team
- Ehsan Eqlimi, PhD | Head of Lab & Founder
Ehsan leads the lab and is interested in developing algorithms to study brain activity using EEG. He focuses on how the brain activity unfolds over time during natural tasks and spontaneous states, combining signal processing, machine learning, and neuroscience. He holds a PhD in Biomedical Engineering from Ghent University, Belgium. He is interested in exploratory research methods.
Outside of research, he enjoys reading philosophy, watching insects and birds, listening to music, and running a YouTube channel in Farsi on philosophy and sometimes signal processing.
- Nada Nouha Aderghal, MSc
Nada is an embedded systems engineer working on research in BCI, neuroscience, and neurology. She collaborates on projects that integrate AI and signal processing for brain-computer interface applications, involving system design, experiments, and data analysis. She is passionate about interdisciplinary research and developing innovative solutions in neuroscience and health technology. Outside of research, she enjoys walking in nature and participating in volunteer scientific clubs.
- Maryam Tahan, MSc
Maryam is a biomedical engineering professional with years of experience in research, data analysis, and interdisciplinary collaboration within health-related fields. Her background includes working in research teams on complex datasets and contributing technical expertise to support scientific investigations.
In recent years, she has also developed strong skills in mentoring, coordination, and effective communication within collaborative environments. She is interested in contributing her skills to rigorous research projects within a team-based setting.
In her free time, she enjoys coaching, dancing, and continuous learning, particularly in areas related to human behavior and wellbeing.
- Parisa Raouf, MSc
Parisa is a biomedical engineer specializing in computational neuroscience, EEG signal processing, and machine learning. Her research endeavors include developing data-driven models for neural signal analysis, with a particular emphasis on mental health applications and brain dynamics. She has employed signal processing techniques, statistical learning, and deep learning methodologies. Her research interests encompass fundamental and exploratory studies of brain activity, sparse representations, and the interrelation between neural dynamics and cognitive processes. More recently, her focus has broadened to include interpretable and explainable models for neural data analysis.
Beyond her academic pursuits, she engages in activities such as playing badminton, cooking, baking, reading, and acquiring new theoretical knowledge, while contemplating the intersections between neuroscience and mathematics.
Lab Ethos
We prioritize open, reproducible, and transparent research. Sharing code, data, and methods is central to our approach. We encourage curiosity-driven research, collaboration, and mentoring, fostering an environment where ideas can be explored rigorously and creatively.
Philosophy
Our philosophy is that the brain should be studied as it operates naturally over time, rather than only through artificial, discrete experiments. Continuous, naturalistic EEG recordings capture the ongoing dynamics of cognition, attention, and perception, offering richer insights into neural computation.
Vision
We aim to understand how neural dynamics unfold over time in both spontaneous and task-driven activity. By integrating EEG with computational modeling, we hope to reveal principles of brain function, bridging basic neuroscience with real-world applications in cognition, learning, and behavior.
Mission
Our mission is to:
- Develop computational tools to analyze continuous EEG data
- Map the temporal dynamics of neural activity
- Provide open-source resources and share methods
- Train the next generation of researchers in computational neurodynamics
Methods
We employ a combination of:
- Continuous EEG recordings for resting-state and task-based paradigms
- Time-series and signal processing techniques
- Machine learning and subspace modeling
- Microstate and oscillatory dynamics analysis
- Naturalistic paradigms such as speech tracking and music listening