Difference between revisions of "ESE297 - Intro to Undergraduate Research"
From ESE497 Wiki
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* [[Project2a: Implement algorithm with sbRIO robots]] | * [[Project2a: Implement algorithm with sbRIO robots]] | ||
* [[Project2b: Put the sbRIO robots in motion]] | * [[Project2b: Put the sbRIO robots in motion]] | ||
| − | * Project3: BCI Offline Analysis | + | * Project3: BCI Offline Analysis - Plot R^2 for all Channels/Frequency Pairs |
**[[media:AnalyzingBCI2000DatFiles.pdf|Analyzing BCI2000 dat Files]] | **[[media:AnalyzingBCI2000DatFiles.pdf|Analyzing BCI2000 dat Files]] | ||
**Start with [[media:OfflineAnalysisSpectral.m|OfflineAnalysisSpectral.m]]. This is the file you will modify for this project. | **Start with [[media:OfflineAnalysisSpectral.m|OfflineAnalysisSpectral.m]]. This is the file you will modify for this project. | ||
| Line 32: | Line 32: | ||
***find | ***find | ||
***spectrogram | ***spectrogram | ||
| + | * Project4: BCI Offline Analysis - Combine best Channel/Frequency pairs | ||
| + | * Project5: BCI Offline Analysis - Analyze your own data | ||
| + | * Project6: Move Robots based EEG signal detection | ||
== Lecture Notes == | == Lecture Notes == | ||
Revision as of 19:05, 20 March 2012
The Robotics Sensing Training Course was created for students who wish to do Undergraduate Research projects in Robotic Sensing under Professor Nehorai, the ESE Department Chair. This course is offered as ESE497 (Section 27) for 2 credits and is typically offered in the spring and summer. Students will learn how to implement sensor array signal processing algorithms on the LabVIEW for Robotics Starter Kit robots shown above using both Matlab and LabVIEW. Students can then apply this knowledge to individual research projects in Robotic Sensing in subsequent semesters.
Logistics
- Meeting Time: Wednesday 8:30-10 pm, Friday 4-5:30 pm in Bryan 316
- Holidays:
- Instructor: Ed Richter
- Faculty Supervisor: Arye Nehorai
- Syllabus
- Expectations: Each student needs to work 10 hours/week in order to earn the 2 credits for ESE497. In the summer, the expectation is 20 hours/week. That is, students who earn an A will spend many unsupervised hours outside of the class meeting times. In Part 1 of the class, homework will be assigned and is due during the next class meeting. The project (Part2 below) should be completed by the end of the semester.
Course Details
- Case Study - Study acoustic source localization using Microphone array (see Lecture Notes below)
- Demonstrations, Background and Theory
- Data Acquisition Basics
- Introduction to Digital Signal Processing Tools
- Project1:_Implement_algorithm_using_microphone_array
- Project2a: Implement algorithm with sbRIO robots
- Project2b: Put the sbRIO robots in motion
- Project3: BCI Offline Analysis - Plot R^2 for all Channels/Frequency Pairs
- Analyzing BCI2000 dat Files
- Start with OfflineAnalysisSpectral.m. This is the file you will modify for this project.
- Open in Matlab. Click on File-Set Path and "Add with subfolders" the Tools folder from the BCI2000 folder
- Click the Run arrow. Select eeg1_1.dat, eeg1_1.dat and eeg1_3.dat. Click Open
- Remove the DC (average) for all 64 channels
- Implement CAR Spatial Filtering using the BCI2000 funtion carFile
- Syntax: signal = carFilt(signal,2) ;
- For every channel compute the power spectrum for every segment of 80 samples every 40 samples for both condition==0 and condition==2 and average them. Create two 3D array of PowerSpectra of size(NumFrequencies,NumChannels,NumTrials) for both conditions.
- Compute the R^2 function for all Channel/Frequency pairs
- Plot the R^2 values for all Channel/Frequency pairs using the BCI2000 function calc_rsqu. It should look similar but not identical to the graph on the tutorial.
- Useful Matlab Functions:
- find
- spectrogram
- Project4: BCI Offline Analysis - Combine best Channel/Frequency pairs
- Project5: BCI Offline Analysis - Analyze your own data
- Project6: Move Robots based EEG signal detection
Lecture Notes
- Topic 1: Acoustic Source Location Background and Theory (Slides 1-19)
- Additional references:
- Homework 1: Read the material that we discussed in our meeting today and the additional references listed above.
- Homework 2: Using this figure, derive the general equations for the source location (x*,y*) which include the rotation of both pair, i.e., the intersection of the 2 lines. Verify that the formula on slide 10 of the lecture notes is correct for the special case where
- y1 = y2 = 0
- Rotation1 and Rotion2 = 0
- X1=P/2
- X2 = -P/2
- Topic 2: Data Acquisition Basics
- LabVIEW Tutorial
- Code up examples in LabVIEW for slides 11, 14, 27, 31, 36, 38, 41. Put each one in a separate VI and demo to me or T/A.
- Configure LabVIEW options as shown in slides 15-17
- Exercises 1,2,3
- Homework 3 - Finish Exercises
- Assign Project1 - Simulation
- Homework 4 - Finish ComputeAngle.vi (in Project1 -> RoboticSensing.zip -> micSourceLocator.lvproj -> My Computer -> ComputeAngle.vi) and ComputeIntersection.vi
- Additional Resources
- Conditionally append values to an array in a loop
- How to Create and Array on the Front Panel
- LabVIEW tutorial, LabVIEW 101
- Data Acquisition Basics
- Homework 5 - Finish exercise
- Homework 6 - Connect wires from A00 and AO1 to AI0+ and AI1+ (remove wire from Banana A to AI0+). Make sure that the Prototyping Power is on. Modify your vi from Homework 5 to collect samples from both AI0 and AI1. Then open DelayedChirp2DAC.vi and run this vi. You shouldn't modifiy DelayedChirp2Dac.vi. Run your modified Homework 5 vi and zoom in in the time and frequency domain to examine the waveforms in detail. Describe in detail what you see. Measure the difference in time between both channels. Hint: Start and stop your Data Acquisition vi until the entire signal is in the middle of the buffer.
- Cross Correlation
- Homework 7
- Plot the Cross Correlation of the 2 channels from Homework 6 and see if the peak is shifted from the middle, the number of samples you measured from the previous step.
- Hints:
- Functions -> Express -> Signal Analysis -> Conv & Corr -> Cross Correlation
- This function requires that you extract the 2 channels from the DDT. To do this, use Functions -> Express -> Sig Manip -> From DDT -> Single Waveform -> Channel 0 and then again for Channel 1. Connect the outputs of these to the X and Y inputs.
- Before you plot the Cross Correlation, extract the 1D array of scalars using the From DDT so that the X-Axis is in samples.
- Look at the help on the Cross Correlation for details.
- Hints:
- Plot the Spectrogram of Channel 0.
- Hint: There is a good Spectrogram example that ships with LabVIEW. Go to Help -> Find Examples... and search for STFT -> STFT Spectrogram Demo.vi. You can copy from this example and paste it into your code.
- Plot the Cross Correlation of the 2 channels from Homework 6 and see if the peak is shifted from the middle, the number of samples you measured from the previous step.
- Homework 7
- LabVIEW Tutorial
- Topic 3: Signal Processing Basics
- Tutorial
- Homework 8- Finish exercise from tutorial.
- Homework 9- Use the Signal Processing Palette in LabVIEW to generate 2 sinusoid waveforms (Signal Processing -> Waveform Generation -> Sine Waveform) with two different frequencies. Add these together and implement 2 separate filters for this signal (Functions -> Express -> Signal Analysis -> Filter) to extract the original sinusoids. Plot these outputs in the time domain. Also, plot them in the frequency domain (Express-> Signal Analysis -> Spectral). Make sure you can identify the frequencies corresponding to the input sinusoids in the frequency domain. Next, add (as in addition) Gaussian White Noise to the sum of the 2 sinusoids (Signal Processing -> Waveform Generation -> Gaussian White noise). Plot the spectrum of the unfiltered signal and identify the frequencies corresponding to signal and noise again. Increase the standard deveiation of the WGN and modify your filter to improve the quality of the filtered signal. Also, looking at the sum of the 2 sinusoids and the noise, what is the relationship between the Standard Deviation of the WGN and the amplitude of the noise. Plotting the histogram of the noise (Express -> Signal Analysis -> Histogram) might help? Note: If your graph X-axis is in absolute time instead of seconds, right click on the graph and select Properties -> Display Format -> X-Axis and set it to SI units.
- DSP Lecture by Dr. Jim Hahn (for reference)
- DSP Configurations Lecture by Dr. Jim Hahn (for reference)
- Tutorial
- Topic 4: Brain Computer Interface (BCI)
- Performing an Offline Analysis of EEG Data using BCI2000
- BCI2000 is installed at c:\BCI2000. The tools and data folders referenced in the tutorial are at c:\BCI2000\tools and c:\BCI2000\data
- To launch BCIViewer, browse to tools\BCI2000Viewer and double click on BCI2000Viewer.exe. Pay particularly close attention to States StimulusBegin and StimulusCode.
- To launch OfflineAnalysis, browse to tools\OfflineAnalysis and double click on OfflineAnalysis.bat
- Introduction to Signal Detection/Classification
- Introduction to EEG Physiology
- How to collect EEG data using BCI2000 and the Emotiv headset
- Performing an Offline Analysis of EEG Data using BCI2000
