BME I5100: Biomedical Signal Processing and Signal Modeling

Lucas C. Parra

Course overview and schedule:
Syllabus

Lectures:
1. Introduction
2. Linear Systems, Impulse Response, ARMA 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
11. Causal Inference, introduction
12. EEG linear decoding and spatial filtering

Data files:

  1. Heart rate during rest and a Stroop task: hr_rest_stroop.mat
  2. ECG signal: ECG.mat
  3. EMG data measured on tongue muscle: tonguemg.mat
  4. Error Related Negativity & Scalp Data: eeg-ern.mat, scalp.m.
  5. Eye blinks (EEG): EEG.mat
  6. Spike trains: spike.mat
  7. Speech (start the day on the right foot: speech.wav
  8. Sine Frequency Estimate: sinefreq.mat
  9. Local field potentials from hippocampal slice (first channel recorded in CA1, second channel recorded outside the slice as noise reference): gamma.mat
  10. 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): lfp.mat
  11. Bird song recorded in the wild in stereo: bird-stereo.wav
  12. EEG Visual evoked responses: eeg-vep.mat. 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..)
  13. EEG to analyze apha power: eeg_128Hz.txt (recorded at 128 Hz)
  14. EEG to analyze apha power, 2nd example: alpha.mat
  15. EEG and audio sound files to analyze the response of EEG to sound envelope: audio_eeg.mat
  16. Simulated data to practice idendtifying cause while contolling for condounding: cause_or_confound.mat
  17. Simulated data to practice computing LATE, ITT, PP estimates in RCT with imperfect compliance: LATE_example.mat
  18. Simulated data to practice estimating direction of forcasting using Granger "causality": granger_example.mat

Tutorials:
Matlab tutorials: several courses from Mathworks, and Open Course Work from MIT
Linear Algebra: Open Course Work from MIT
Linear Algebra: Compact review and nice visualization of liner algebra
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, ...)

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