This course is designed to introduce neuroscience and biomedical engineering students to the basic signal processing techniques useful for analysing neuroscience data. The goal is to provide students with the background required to understand the principles of commercially available analyses software, as well as to allow them to construct their own analysis tools in a programming environment such as MATLAB. 1. Understand and utilize signal representations for continuous time and discrete time signals in both the time and frequency domain. 2. Understand and utilize signal averaging techniques. 3. Characterize and analyze systems in the time domain (differential and difference equations, impulse response) and in the frequency domain (Fourier, Laplace, Z transforms). 4. Understand the chain of events for measuring biological signals from the sensor, through signal sampling and reconstruction, practical sampling and quantization, to the graphical representation and quantification of the signals. 5. Understand and utilize the discrete Fourier transform and the FFT algorithm. Implement the windowed/averaged transform for spectral analysis of signals. 6. Utilize digital filters and design both finite impulse response and infinite impulse response filters. 7. Understand and utilize spike train analysis, autocorrelation functions, wavelet functions, and basic nonlinear signal processing techniques. 8. Gain proficiency with Matlab, and utilize this language to solve problems on a wide-range of signal processing scenarios.

Classroom lectures. Face-to-face classes: reduced (workshops, seminars, case studies). Student individual work. Laboratory session. Final exam. Seminars and lectures supported by computer and audiovisual aids. Practical learning based on cases and problems, and exercise resolution. Individual and group or cooperative work with the option of oral or written presentation. Individual and group tutorials to resolve doubts and queries about the subject. Internships and directed laboratory activities.