[Summer’16 Internship] Arduino, EEG, and Free Will

By Patrick Glover

EXPERIMENT

For this Experiment you will need.

• 1 × Arduino Uno
• 1 × Backyard Brains EMG Spikershield
• 1 × Backyard Brains Heart/Brain Shield
• 1 × Headband
• 2 × Snap fastener studs
• 4 × EKG adhesive electrodes
• 1 × Conductive electrode gel
• 1 × MATLAB

Assembly of the recording device is quite simple. The EMG and EEG signals each come from their respective Arduino shields, both sold by Backyard Brains fully assembled. If you’re interested in building everything from scratch, the schematics are online here and here.

1. Insert the Heart/Brain shield directly down onto the Arduino so that all the male pins fit into the board’s female ports. We want the EEG signal being sent out on channel 1, so short the two pins on analog 1 on the Heart/Brain shield
2. Our goal is to stack all three boards, but the signal output jacks on the Heart/Brain shield block the EMG shield from fitting effectively. To fix this, we simply add pin header extenders to all the EMG shield’s pins. The boards should stack without any issues. We want EMG signal on channel 2, so short both pins on analog 2 on the EMG shield.
3. Once the shield stack is assembled, load the spike recorder code onto the Arduino, found here. Also, download the free Backyard Brains Spike Recorder software.

Here is the final product

The next step is to make a bidirectional, one channel EEG headband. To do this, take an elastic headband and insert two snap fasteners roughly 4 cm away from each other on the midline of the band. Any type of headband should work, but avoid any with metallic paints or patterns.

On your subject, secure the headband vertically on the front of their head so that the flat faces of the two metal studs are on C3 and Cz.

Apply a dab of electrode gel underneath each lead to help conduct signal. This is especially important for subjects with longer hair. Place a single electrode on the left mastoid process. This will be our reference electrode.

Place one electrode on the back of your subject’s right hand, and two on the inside of their forearm, spaced around 15 cm apart, right over the ulnar nerve.

Plug in an orange muscle electrode cable to each of the shields, and attach alligator clips to your subject. On the EMG board, connect the black clip to the back of the hand, and red clips to the ulnar nerve electrode. On the EEG board, connect the black clip to the mastoid electrode and the red clips to the electrodes on the headband. Polarity is not important.

Connect your Arduino to the computer over USB, and open the Spike Recorder app. In settings, connect to the USB modem by selecting it in the drop down menu and clicking the plug icon. Once connected, go back to settings and select two channels. You can also adjust band pass filter settings as needed. You are almost ready to begin recording data.

Verify that your signals are EEG and EMG. To do this, have your subject relax their arms on a table, and have them flick their wrist several times. You should see one channel showing significant spiking any time the subject does this. In this particular recording, EEG signals are harder to verify. The presence of alpha waves is greatly diminished over this region of the scalp. One way to verify the signal is valid is to have your subject quickly direct their eyes up and then return to the center. The head should not move – only eyes. Have them look down, center, left, center, right, center. The EEG should register electroocular artifacts. If your resting signal looks similar to below, you may proceed with the test.

Have your subject sit upright and look straight ahead, arms resting on the table. Click the record button on Spike Recorder. Instruct them to briefly and deliberately flick their right wrist any time they feel like doing so. Flicks should be at least four seconds apart. They should not fall into a mindless rhythm — each flick should be intentional. Record as many trials as you see fit, although I’ve noticed I only get a decent response when I’m looking at at least 100 flicks. Click the record button to finish your recording. The software will tell you where it saved the file.

Find the .wav file (It’s in /User/Music/Spike Recorder on Mac) and copy it into your MATLAB project folder. In the runRP MATLAB script, change the file name on line 1 to the name of the file you would like to analyze. In the command window, type runRP. The script will return several figures. All MATLAB code can be found herehttps://github.com/patrick-glover/EEG-Code

By Patrick Glover

[Summer’16 Internship] Arduino, EEG, and Free Will: Measuring awareness times

Libet had the subjects view an oscilloscope with a dot rotating periodically in a circle, and subjects were asked to retroactively report where the dot was when they first became aware that they were about to perform the voluntary task. He could then calculate the average latency between when the subject reported awareness and when the action actually occurred. I was able to replicate this setup in a more DIY manner.

I used a continuous rotating servo to turn a balsa wood arm with a period of 1.2s per rotation. Each time the arm of the clock passed over a photoresistor, a custom circuit I put together would return a digital low to the third channel of the arduino that’s already recording EMG and EEG on channels 1 and 2. The drop in sample rate to 3333 Hz isn’t an issue.

Unfortunately the clock needs an additional Arduino to drive the servo, since the data logging arduino can’t put out enough power to do everything at once.

Here’s the schematic.

Modifying the MATLAB code to detect the clock passing the 12:00 position was dead simple. The only inconvenient part of this new workflow is the researcher must manually write down all of the integer times reported (1-12) for each wrist flex and enter them one by one during the analysis. I’ll work on streamlining it.

Here’s the final result. Mean latency was 218 ms between awareness and action, with a significant RP showing up right as Libet documented at around 1 second before action. Monte Carlo (dotted blue) bars are 2SD from the mean signal (not plotted).