We are always excited to welcome motivated students and researchers to our team. If you're passionate about what we do and are interested in joining our lab, we’d love to hear from you!
Contact us at: [email protected]
We are always excited to welcome motivated students and researchers to our team. If you're passionate about what we do and are interested in joining our lab, we’d love to hear from you!
Contact us at: [email protected]
I am a PhD student in Biomedical Engineering with a research focus on the neural mechanisms of memory consolidation during sleep. My work investigates the dynamic interplay between slow oscillations and sleep spindles, integrating EEG-based sleep analysis with behavioral memory paradigms. I'm particularly interested in how distinct features of non-REM sleep contribute to long-term memory stabilization, and how electrical and auditory stimulation can further enhance this process.
As part of an international collaboration with Professor Lisa Marshall's team at the University of Lübeck, my research employs SOtdcs (slow oscillation transcranial direct current stimulation) and closed-loop auditory stimulation to selectively enhance sleep rhythms that support memory consolidation. Our goal is to understand how these precisely synchronized stimulations can modulate the strength and timing of fast and slow spindles in coordination with slow oscillations, ultimately improving overnight memory retention.
I'm also deeply committed to translating these findings into non-invasive cognitive enhancement strategies for educational and clinical applications. My aim is to bridge basic neuroscience with real-world interventions that can support learning and memory across diverse populations.
Selected Publications
Slow spindles are associated with cortical high frequency activity (NS Hashemi, F Dehnavi, S Moghimi, M Ghorbani — NeuroImage, 2019)
Opposite effect of motivated forgetting on sleep spindles during stage 2 and slow wave sleep (F Dehnavi, S Moghimi, S Sadrabadi Haghighi, M Safaie, M Ghorbani — Sleep, 2019)
Spontaneous slow oscillation—slow spindle features predict induced overnight memory retention (F Dehnavi, PC Koo-Poeggel, M Ghorbani, L Marshall — Sleep, 2021)
Memory ability and retention performance relate differentially to sleep depth and spindle type (F Dehnavi, PC Koo-Poeggel, M Ghorbani, L Marshall — iScience, 2023)
Motivated forgetting increases the recall time of learnt items: Behavioral and event related potential evidence (SS Haghighi, M Ghorbani, F Dehnavi, M Safaie, S Moghimi- Brain Research 1729, 146624)
My research specializes in brain signal analysis and computational neuroscience, focusing on neural oscillations and their functional significance across different brain states. My expertise includes developing algorithms for detecting various brain rhythms, creating methodologies for spike sorting, and building computational models that generate neural oscillations. Currently, I'm working on seizure detection in epileptic marmosets through analysis of brain recordings under supervision of Dr. Maryam Ghorbani and Dr. Igor Timofeev. For updates on my latest research, please visit:
https://github.com/azarmehriAs a biomedical engineering PhD student, I use deep brain stimulation to study how electric fields interact with neuronal activity. Deep brain stimulation is a powerful method for probing and modulating neural circuits, but the precise ways in which electric fields interact with neuronal activity are still not fully understood. In my research, I use deep brain stimulation in anesthetized rodents to study how electric fields influence neuronal behavior, combining these experiments with high-resolution electrophysiological recordings to explore the dynamics of neural populations in vivo.
I am a PhD student in Biomedical Engineering working under supervision of Dr. Maryam Ghorbani and Dr. Sahar Moghimi. My research focuses on modeling and analyzing brain signals, specifically EEG recordings from preterm infants and high-density extracellular recordings in newborn mice and rodents. I am particularly interested in extracting key features from these neural signals to understand how auditory stimulation at different frequencies affects brain activity across various regions. Notably, the brain’s response to such stimulation varies with age in infants, and by identifying important signal characteristics, I aim to elucidate these developmental differences. Combining clinical insights with advanced electrophysiological techniques, my work bridges basic neuroscience and potential therapeutic applications, such as optimizing auditory stimulation protocols to enhance cognitive function or treat neuropsychiatric conditions. Through this integrative approach, I strive to uncover the underlying neural mechanisms that govern how external auditory stimuli modulate brain networks during critical periods of development.
Neural replay during sleep is a key mechanism thought to support memory consolidation, yet many details remain unclear. I am a direct PhD student in Physics working under supervision of Dr. Maryam Ghorbani and Dr. Alireza Valizadeh. My research focuses on sequential activity and replay during sleep, and their relationship with sleep-related brain oscillations, particularly in the cortex. The project involves two main components: computational modeling and analysis of electrophysiological data from the rat cortex. I welcome opportunities to connect with researchers in related areas.
I am a direct PhD student in Physics working under supervision of Dr. Maryam Ghorbani and Dr. Alireza Valizadeh. My work focuses on developing a computational model involving the hippocampus, thalamus, and cortex to study memory consolidation processes such as sequential activity, reactivation, and replay during the phase-amplitude coupling of sleep rhythms, particularly sharp-wave ripples and thalamocortical spindles. In parallel I analyze experimental data to compare and validate the model’s results with experimental observations.
With a background in biomedical engineering (bioelectric), my research focuses on neural dynamics at the boundaries of consciousness, using electrophysiological recordings and targeted electrical stimulation in rodent models. I previously analyzed LFP and spike data in the owl’s IPC region to examine how brain state affects neural responses and have hands-on experience with neural recordings in macaque and marmoset monkeys. Motivated by a desire to bridge engineering and medicine, I aim to contribute where conventional clinical approaches fall short—by applying engineering and neuroscience to advance diagnosis, treatment, and rehabilitation. I have also been teaching part-time at the university level for the past two years.
Amirkhosro maleki is a Ph.D. student in Electrical Engineering with a specialization in Bioelectrics. His research interests focus on brain signal processing and the application of artificial intelligence in neural data analysis. He is passionate about exploring advanced computational techniques to better understand brain function and consciousness, and contributes to projects involving EEG analysis and machine learning-based modeling in neuroscience.
I received my BSc in Biomedical Engineering from Islamic Azad University of Mashhad and completed my MSc at Ferdowsi University of Mashhad under the supervision of Dr. Sahar Moghimi and Dr. Javad Safaei. During my master’s studies, I worked on projects involving the analysis of NIRS and EEG signals during sleep. I am currently pursuing a PhD under the supervision of Dr. Ghorbani, where my research focuses on EEG analysis and auditory stimulation during sleep. My interests lie in understanding the neural mechanisms underlying sleep and the interaction between external stimuli and brain dynamics, with the broader aim of exploring how brain activity during sleep contributes to cognitive and physiological processes.
I hold a Bachelor's degree in Biomedical Engineering from the University of Birjand and I am currently pursuing my Master's degree under the supervision of Dr. Maryam Ghorbani. My current research focuses on the analysis of EEG signals, particularly in response to transcranial electrical and auditory stimulation during sleep. I am also involved in integrating pupillometry data with EEG signals to investigate levels of alertness using eye-tracking systems. More broadly, I am interested in understanding how physiological signals reflect brain states and how external stimulation can modulate these states in humans. Additionally, I have a strong interest in the design and development of biomedical engineering devices.
I am a researcher in biomedical engineering, fascinated by how rhythmic electrical activity coordinates communication across the sleeping (and awake) brain—and by how we can harness that knowledge to improve memory and treat disease. After earning my B.S. in Biomedical Engineering from Islamic Azad University of Tehran, I joined Dr. Maryam Ghorbani’s lab at Ferdowsi University of Mashhad for my M.S. There, I combine hands-on rodent surgery with transcranial brain stimulation to investigate whether externally imposed sleep rhythms can modulate neural dynamics involved in memory processing.
Daniel holds an M.Sc. in Neuroscience from Ferdowsi University of Mashhad. His thesis focused on the spatial distribution of sleep spindles in the AD thalamus and their connectivity with the hippocampus. By classifying spindle and slow wave types and analyzing their temporal coupling, he studied thalamocortical-hippocampal interactions. His interests include brain connectivity, learning, memory, and cognitive neuroscience.Currently, he is working at the School of Medicine, Mashhad University of Medical Sciences.
I am an Electrical Engineer specializing in Biomedical Engineering, with experience in developing ultrasound imaging systems and intelligent rehabilitation technologies. My research includes noninvasive retinal stimulation with ultrasound for visually impaired individuals, bone implant fixation detection, and robotic knee rehabilitation—conducted at Ferdowsi University and the University of Southern California. I focus on advanced signal processing and algorithm design for imaging, with strong interests in high-frequency ultrasound, neuroscience applications, and AI-driven health technologies.