K14: Can describe ideas using mathematical computational models, and is conversant with their programming languages, the functioning of neural operating systems, and their posible interactions with external devices. S1: Uses a variety of techniques to find, manage, integrate and critically evaluate available information for the development of professional activities in Neuroscience, especially in the digital sphere S4: Uses their ability to analyse and synthesise, as well as to apply the principles of the scientific method in the work environment, in order to provide innovative responses to the needs and demands of society in their area. S5: Appropriately uses the scientific and technical vocabulary of the different subfields within Neuroscience. S7: Comprehends the computational and experimental tools used for analysis and quantification of neuroscience data, and can appropriately apply these tools to significant problems in neuroscience. C2: Apply knowledge about the organisation, structure and function of the Central Nervous System (CNS) to contribute to the evolution and improvement of technologies and systems for computing, data handling and analysis. C3: Apply knowledge about technologies for the study of the Nervous System and the brain (Medical Imaging, brain-machine interfaces) to develop new systems for diagnosis and treatment, as well as and other applications within Neuroscience (Artificial Intelligence, Robotics) with the aims of improving the quality of life and furthering social progress. C4: Uses advanced mathematical, statistical and computational tools to increase and improve knowledge in neuroscience and its applications. C5: Apply your neuroscience knowledge in a unifying and integrated fashion as part of a multidisciplinary team (pharmaceutical sector, health industry, diagnostic techniques, health information technologies, government agencies and regulatory bodies. C6: Apply the results of your comprehensive training to your everyday professional activities, combining Neuroscience knowledge with a solid foundation of ethical responsibility and respect for fundamental rights, diversity and democratic values. C7: Apply the scientific and technical principles you acquired during your undergraduate training, together with your own natural learning capabilities, to better adapt to novel opportunities arising from scientific and technological development.

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.