Lab 03_EEG

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Ohio State University *

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4110

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Electrical Engineering

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Apr 3, 2024

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BIOMEDE 3702 LAB PROCEDURE: EEG Learning Objectives: Students will be able to…  Record an EEG from an awake, resting subject under different conditions.  To examine differences in the level of alpha rhythm activity during mental arithmetic and hyperventilation, compared to the control condition of eyes closed and relaxed. Materials:  Arduino Uno + USB A/B  Adafruit USB Isolator + USB Micro  EEG Exposed Circuit  Lycra ® swim cap (such as Speedo ® brand) or saran wrap or an elastic band.  Disposable Electrodes (3 electrodes per subject)  BIOPAC Electrode Gel (GEL1) and alcohol prep  Matlab Software  Laptop  Wires Experimental Methods: A. ARDUINO SETUP AND CONNECTION 1. Connect the Adafruit USB isolator to the computer using the USB micro. Note: This should be done first to avoid unpleasant shocks. 2. Connect the Arduino Uno to the USB isolator using the USB A/B. ( Figure 1 ). Figure 1: Complete Arduino connection for the lab. A. ARDUINO CALIBRATION The Arduino and MATLAB calibration steps are to ensure that both the components are communicating with each other and record the correct signals.

BIOMEDE 3702 1. Go to Carmen and download the lab code files mentioned below. ( Carmen > Important Links > Lab Links and Documents ). 2. For this lab you will need the Arduino codes: Arduino_Calibration.ino and Arduino_One_Channel_Acquisition.ino . 3. Open the Arduino_Calibration.ino sketch. 4. Arduino will possibly ask to place this file in a folder, select yes. 5. In the Arduino Integrated Development Environment (IDE - window) you will see a code. 6. On the top tool bar, select Tools >> Port >> COMX (Arduino UNO) (Figure 2) i. Let TAs know if you don’t find this so we can troubleshoot. ii. For Macs: Go to Tools >> Port >> select “/dev/cu.usbmodem14201 (Arduino Uno)”. This is your COM port name. Figure 2: Selecting a COM port. 7. Upload it to your Arduino by pressing the right facing arrow. ( Figure 3 ).

BIOMEDE 3702 Figure 3: Sketchbook main buttons. The right facing arrow (in red) is the upload button. 8. At the bottom of the IDE window you will if the upload was successful. The lower right will show you what COM port is connected (Figure 4). If an error was found, disconnect and reconnect the USB. Figure 4: IDE upload confirmation at the bottom of the screen and current COM port B. MATLAB CALIBRATION The MATLAB calibration is to ensure that the Arduino signals and Matlab are communicating correctly through the serial port. For this step you will be reading the Arduino produced signal in Matlab. 1. Locate the Matlab script called Arduino_Read.m . 2. In Matlab’s command window call the function with the COM Port (the Arduino IDE, at the bottom right, will tell you the COM port) . Also, input a 1 to denote two channels. 3. In Matlab >>Arduino_Read(6,1) %For a COM# Arduino and 1 channels. i. For Macs: In Matlab>> Arduino_Read('/dev/cu.usbmodem14201',1) %For a COM port Arduino and 1 channels. 4. Select 5 seconds and do not save the output (it is not necessary for this lab). The output should look like Figure 5 .

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BIOMEDE 3702 Figure 5: Arduino Calibration Matlab Output C. SENSOR PLACEMENT In this lab we will be using the long three connector leads (red, blue, and black - RBB). These leads will be connected to electrodes in different areas of the head so keep close attention to them. 1. Position electrodes on the scalp. Figure 6 shows a sample configuration. Use the blue lead instead of the white lead. Figure 6: Electrode Placement Hints for obtaining optimal data :  As much as possible, move (part) the hair away from the electrode adhesion area to ensure the electrode makes contact with the scalp. Place the ground electrode on the earlobe instead placing it behind the ear for better impedance values.  Apply a tiny drop of gel to the electrode.  Apply pressure to the electrodes for about one minute after the initial placement.

BIOMEDE 3702  Subject must remain still. Blinking and other movement will affect the recordings of all four rhythms.  Despite your best efforts, electrode adhesion may not be strong enough to record data; try another Subject or different electrode placement. 2. Clip the Electrode Lead Set following the color code in Figure 6. 3. Place swim cap/wrap on Subject’s head to press electrodes into scalp (Figure 7). Figure 7: Bandage (or Swim Cap) placement 4. Get Subject in proper seating position 5. Wait five minutes to allow Subject to relax, and for electrodes to establish proper contact. 6. Connect the circuit following Figure 8. Make sure you are very careful with this circuit as components are not fixed in the breadboard.

BIOMEDE 3702 Figure 8: Circuit connection to Arduino D. DATA RECORDING Overview: i. You will plug yourself to the Arduino using the built EEG circuit. ii. You will perform four experiments. 1. Experiment 1 : Record a control 15 second recording while the subject is relaxed with eyes closed. 2. Experiment 2 : Record a 15 second recording while the subject performs mental arithmetic with eyes closed. Make sure you have a problem set ready . 3. Experiment 3 : Record a 15 second recording as the subject recovers from hyperventilating with eyes closed. Only hyperventilate for 30 seconds, no more. 4. Experiment 4 : Record a 15 second recording as the subject is relaxed with eyes opened. iii. You will be processing the resulting EEG signals and analyzing using statistics. Hints for obtaining optimal data:  Subject must try not to blink during “Eyes Open” portion of recording.  Subject should not talk during any of the recordings, and should not verbalize answers to the mental arithmetic.  The alpha signal will be increased during the relaxation recording if Subject relaxes mentally; i.e. thinks of a relaxing place.

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BIOMEDE 3702 Relaxed with eyes closed (Control) 1. Prepare subject for recording. Subject must remain seated, relaxed, and still, with eyes closed. 2. Upload the Arduino_One_Channel_Acquisition.ino and then start the Aruino_Read.m . 3. Record for 15 seconds. 4. Save the data. Remember not to use special characters or spaces in naming it. Figure 9: Example Data (Relaxed) 5. It is normal for data to be noisy. It should have a combination of slow and fast waves. We will process the signals at the end. If your result does not resemble this, tell a UTA/GTA or Instructor. Mental Arithmetic 6. The student at the computer will prepares a math problem. The subject will remain seated and relaxed, with eyes closed. Review the following before recording: The problem should be challenging but not too difficult - the point is to make Subject really work for the answer, not to stump the Subject. Create this problem set prior to recording. For example: 2 minus 4…times 3…plus 9…double that…double again…divide by 4... 7. Record for 15 seconds while verbalizing the math problem to the subject. The subject has to solve the problems silently with eyes closed. 8. Save the signal. 9. If the Subject indicates the math problem was given too quickly, Redo the recording.

BIOMEDE 3702 Hyperventilating Subject will begin hyperventilating (by breathing rapidly and deeply through mouth) for 30 seconds with eyes closed. It is important that recording is resumed as quickly as possible after Subject has hyperventilated. However, to avoid EMG artifact, make sure Subject has stopped hyperventilating prior to clicking Record. 10. As soon as Subject stops hyperventilating and is sitting still, record a 15 second window immediately. 11. Save recording. 12. It is expected for this signal to be noisier than usual. Just make sure that you have a signal similar to Figure 9. Eyes Open recording 13. Subject remains seated and relaxed. 14. During this recording the subject must keep their eyes opened at all times without blinking. 15. Run the script and record for 15 seconds. 16. Save the signal. E. DATA ANALYSIS 1. Download the analysis codes on Carmen . 2. First unmute your computer and increase the volume. Then call the sound function (e.g. sound( EEG .v, EEG .fs) ). This will play your signal through audio. 3. Run the frequencyDomain.m on each of the signals to inspect them. Include an x axis limit (xlim([0,60]). You will surely notice a peak in the 60 Hz range, which we will need to remove later. i. The script has only one parameter frequencyDomain( struct ). i. INPUTS: 1. struct – struct file recorded from Arduino. (e.g. mathEEG) ii. OUTPUTS : 1. f – frequency vector (x axis for the frequency domain). 2. P1 – power in the frequency domain (y axis for the frequency domain). ii. Note: It is useful to use xlim([0,50]) after frequencyDomain() to get a zoomed frequency spectrum.

BIOMEDE 3702 4. Run the eegProcessing.m code. This will implement a bandpass filter between 1 – 50 Hz, as well as a 60 Hz notch filter. It will automatically draw a new figure (internally using frequencyDomain). i. The script has only one parameter: eegProcessing( struct ). iii. INPUTS: 1. struct – struct file recorded from Arduino. (e.g. mathEEG) iv. OUTPUTS : 2. filtered – struct variable with the filtered signal (filtered.v ). v. These details are in the function file. 5. After processing (using eegProcessing ). Run the windowedEEGAnalysis.m code . i. The script has two parameters windowedEEGAnalysis( struct, s ). i. INPUTS: 1. struct – struct file recorded from eegProcessing. 2. s – size of the window (in seconds). ii. OUTPUTS : 3. eegBandMean – mean power for each frequency band. a. Columns: 1- Delta | 2- Theta | 3- Alpha | 4- Beta 4. eegBandMax – maximum power for each frequency band. a. Columns: 1- Delta | 2- Theta | 3- Alpha | 4- Beta iii. These details are in the function file. ii. This code will calculate the mean and max peaks for each EEG frequency band. You will input how long each window will be in seconds ( s – parameter). Ideally s = 1, for a number of segments equal to how many seconds the recording is. iii. These 15 points represent a one second bin from the recording. Select the middle 10 points to avoid noise (big movement artifacts, disconnections, etc). Record them in your Post lab for each condition. F. THE POINT In this lab, we were able to record a signal from the brain using our equipment. The signal was specifically from the parietal region of the brain and focused on a specific frequency (8-13 Hz or Alpha Rhythm). We collected this signal under four conditions and analyzed the data to uncover what differences are detectable between conditions. This will show how the Alpha Rhythm relates to certain attention-related events in the brain. Learning Objectives: Students will be able to…  Record an EEG from an awake, resting subject under the different conditions.

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BIOMEDE 3702  To examine differences in the level of alpha rhythm activity during mental arithmetic and hyperventilation, compared to the control condition of eyes closed and relaxed.

BIOMEDE 3702 POST LAB: EEG ELECTROENCEPHALOGRAPHY ( 20 points) Amplitudes Complete Table 1 with the recorded data in the experimental conditions. Run the windowedEEGAnalysis.m on each condition and set the second parameter (s) to 1. This will give you the mean and max frequency power for all EEG bands. You only will need the Alpha Band which is the third column of the output array. (2 point) Table 1 Alpha – Mean Frequency-band Power Segmen t Eyes Closed Mental Arithmetic Recovery (Hyperventilation) Eyes Opened 1 2 3 4 5 6 7 8 9 10 1. You will be using an ANOVA statistical test to tell if the experimental conditions are different, and even identify which are different. (i) Run an ANOVA analysis using the mean Frequency-band value as a response, and each experimental condition as a factor. Report the ANOVA’s p-value and discuss the results. (3 points) (ii) Perform a Tukey’s post-hoc test? Are any of the conditions different to eyes closed (control)? (3 point) (iii) Based on your expectation from your knowledge (e.g., class/lecture), do your results match the theory? Why or why not? (3 point) 2. Did subject need to concentrate during math problems? Yes No How would the level of concentration required affect the data? (3 points)

BIOMEDE 3702 3. Base on the data and the statistical results: What conditions produced the lowest alpha activity? What was your expectation? (3 points) 4. How might the signal-to-noise ratio affect your analysis? (3 points) Challenge Question 3: Note: Challenge questions in post-labs are bonus and optional. These questions are designed to be challenging. Please submit your attempts here on Carmen. 2. Write a detailed protocol on how to analyze a P300 event-related potential (ERP), specifically under the oddball paradigm.

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BIOMEDE 3702

Lab 03_EEG

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