AI & Data Science Intern - Cognitive Science

Conducted innovative research analyzing brain activity in individuals with varying levels of trait anxiety (low vs. high) using EEG data. Utilized Python, MNE, and time series analysis to preprocess, visualize, and interpret EEG signals, identifying key differences in neural activity patterns associated with different anxiety levels. Applied advanced data science techniques to extract meaningful insights, contributing to a deeper understanding of the neural mechanisms underlying anxiety. Collaborated with a multidisciplinary team to enhance the robustness of data analysis methodologies, ensuring high accuracy. Future plans include presenting research findings to the academic community, showcasing the implications for future anxiety-related cognitive neuroscience research and potential therapeutic approaches.