Introduction to Signal Processing in Matlab

The course will provide an introduction to signal analysis in Matlab. The course will include selected topics within signal processing like digital data sampling, signal-to-noise ratio, data filtering, selection of time periods, and calculation of relevant signal characteristics. The course will include introduction to basic operations in Matlab such as importing and exporting data, data visualization, and programing of simple logical structures, use of functions, indexing of data vectors and matrices, and batch processing of larger data sets. The course will use exercises and examples to show the use and interpretation of signal analysis in movement and neuroscience

After completing the course BEV3201, the student is able to:

• Explain important aspects of signal processing like data sampling, signal-to-noise ratio, data filtering and selection of time periods, and how these factors affect calculation of relevant signal characteristics.
• Apply basic operations in Matlab such as importing and exporting data, data visualization, programming simple logical structures, indexing data vectors and matrices, and batch processing large data sets.
• Create a Matlab script that can read the data, improve data quality, visualize results and compute relevant signal characteristics of various signals relevant in movement and neuroscience
• Perform signal analysis using basic operations in Matlab.
• Explain central concepts of signal processing - Understand the importance of various aspects in signal analysis for data quality and further statistical analysis

The course contains eight meetings of up to 5 hours which includes some lectures, but most practical exercises in Matlab. Students are expected to work on assigned tasks between the meetings and participate in all activities. The course is taught in English when required and all communications must be in English if international students are enrolled.