BME I5100: Biomedical Signal Processing and Signal Modeling

Lucas C. Parra

Course overview and schedule:
Syllabus

Homework Instructions:
1. Batch LMS ARMA modeling
2. Linear System Responses
3. Source separation

Data files:

  1. Eugene N. Bruce, Biomedical Signal Processing and Signal Modeling, John Wiley & Sons, 2000: download files
  2. Error Related Negativity: eeg-ern.mat, scalp.m.
  3. Eye blinks: EEG
  4. Spike trains: simulated
  5. Speech: .wav file
  6. Sine Frequency estimate: .mat file
  7. Local field potentials from hippocampal slice (first channel recorded in CA1, second channel recorded outside the slice as noise reference): .mat file
  8. Local field potentials from hippocampal in-vivo recodings (first 60 second are baseling activity, remaining time includes responses to electrical stimulation, time markers indicate start of electrical stimulation): .mat file
  9. Bird song recorded in the wild in stereo: .wav file
  10. Bird song recorded in the wild in stereo: .wav file
  11. EEG Visual evoked responses: .mat file. To display this data you may use topoplot() and this location file. (The version of topotplot() posted here is VERY old, but has the distinct advantage of not requiring enything else other that this one single file. If you want a newer, significantly more complicated version of topoplot you will have to install the complete EEGLAB toolbox from UCSD..)
  12. EEG to analyze apha power: .txt file
  13. EEG to analyze apha power, 2nd example: .mat file

Tutorials:
Matlab tutorials: several courses from Mathworks, and Open Course Work from MIT
Linear Algebra: Open Course Work from MIT
Digital Signal Processing: graphic demonstrations in matlab
Andrew Ng's lectures on machine learning: videos and course page.
Geoffrey Hinton's neural networks for machine learning: Coursera class
Digital signals processing: Coursera class

Tools:
A fair number of good algorithms: netlab (Logistic regression, mixture EM, PCA, ...)

Lectures:
1. Introduction
2. Linear Systems, Impulse Response, ARMA filter
errata: to model an i/o relationship with an AR filter use: IIR adaptive filter.
3. z-transform, fourier transform, filter design, circular convolution, sampling theorem
4. Random Variables
5. Jointly Distributed Random Variables
6. Stochastic Processes, Power Spectrum, ARMA model, linear prediction
7. Probablistic Estimation, Harmonic process
8. Linear discrimnation, Single trial detection in EEG/MEG
9. Linear Mixtures, ICA, PCA
10. Kalman Filter, Hidden Markov Model