This course offers a comprehensive exploration of statistical methodologies and signal processing techniques essential for analyzing neuroscience data. This course delves into the intricacies of statistical analysis, covering hypothesis testing, regression, and multivariate methods tailored to neuroscientific research. Additionally, students will master different signal processing techniques to uncover valuable insights from diverse neural signals. Through hands-on experience and theoretical understanding, learners will gain proficiency in computational algorithms, enabling them to process and analyze neural data effectively. This course equips aspiring neuroscientists with indispensable tools to navigate and contribute to the evolving landscape of neuroscience research.

CILO-1: Students will be able to apply basic and advanced statistical methods to analyze neural data, including hypothesis testing, regression analysis, and multivariate techniques. CILO-2: Students will be able to utilize signal processing techniques such as filtering, spectral analysis, and time-frequency analysis to extract relevant information from neurophysiological signals. CILO-3: Students will be able to critically evaluate the strengths and limitations of different statistical and signal processing approaches in the context of neuroscience research. CILO-4: Students will be able to design and implement computational algorithms for processing and analyzing neural signals, demonstrating proficiency in relevant programming languages and software tools.