Anahit H
- Research Program Mentor
PhD at Tuebingen Univercity
Expertise
Neuroscience and brain behavior research; Biophysics; Human physiology and anatomy, neuroanatomy; Brain computer interface and EEG based research; Cell biology and genetics; Scientific research design and experimental methodology; Data interpretation; Scientific writing and research communication; AI literacy and critical evaluation in STEM Interdisciplinary research in biology, engineering, and psychology; Undergraduate research and project development; Retinal and Optogenetics
Bio
I am a neuroscientist and lecturer with a background in biophysics, focused on understanding how the brain processes information across cellular and systems levels. My research includes retinal plasticity, neuron glia interactions, and brain computer interface studies using EEG to decode and apply neural activity. I have worked on retinal restructuring after photoreceptor damage and glial cell function in neural signaling, alongside developing BCI systems for controlling external devices. I integrate this research into my teaching by guiding students through experimental design, data analysis, and scientific communication, helping them build independent research projects. Outside of academia, I stay engaged with my community through teaching and mentoring students from diverse backgrounds, including K through 12 programs. I enjoy photography, ethnic music and dance, and traveling, which help me stay connected to different cultures and perspectives. I value creating spaces where students can explore science beyond the classroom and develop confidence in their own path in STEM.Project ideas
Can EEG signals be used to predict intended movement for prosthetic arm control
In this project, students will explore how EEG brain signals can be used in brain computer interface systems for prosthetic arm control. Students will begin with a focused literature review on non invasive EEG, motor intention, P300 or motor imagery paradigms, and current approaches to controlling assistive devices. They will compare methods used in published studies and identify what types of tasks, signals, and analysis strategies are most effective. For the data analysis component, students can work with publicly available EEG datasets related to motor imagery or movement intention. They will learn how to organize EEG data, identify basic signal features, compare brain activity across task conditions, and visualize patterns linked to imagined or intended movement. The final product can be a scientific poster, research proposal, or short review paper with sample data analysis. This project is appropriate for students interested in neuroscience, biomedical engineering, psychology, medicine, or data science. It can be completed fully remotely and scaled based on the student’s experience with coding and statistics.