Biological Signal Processing

ES 155 is the first course on Biological Signal Processing, the science of collection, representation, manipulation, transformation, storage of biological signals and the use of modern scientific computing tools to interpret biological signals and tell engaging and informative stories using biological data. The spirit of the class and example student projects can be found below

Topics include general properties of common biosignals, Bioelectrical (electrophysiological), Biomechanical, Biomagnetic , and Biochemical signals, Bioelectrical acquisition process. Brief discussion of bio-signals obtained from tomography and inverse imaging. Brief introduction to underlying principles of MRI, Ultrasound, CT-Scan, PET, and SPECT, and their associated signals, inverse imaging, ill-posed problems and regularization. Non-transformed and transformed methods for biosignal processing. Structural and Graphical descriptions. Overview of Fourier transforms, Sine and cosine transform, Wavelet transform, Principle Component Analysis, dimension reduction techniques. Blind Source Separation, Representation models based on the statistical independence of the underlying sources, Independent component analysis (ICA), Dependent component analysis, Independent Subspace separation, Pattern Recognition, neural networks, clustering, and genetics algorithms. Applications to Biosignal Processing, and Human computer interaction.