I didn’t write a lot of blog posts this summer because I didn’t have my own research project, but the other research projects kept me plenty busy. I converted over an old BYB library written in a pricy programming language called Matlab into a free open source language called Python. I also cleaned it up and commented out all of the code while I was at it.
I had the privilege of helping out the other fellows with their projects. I got to be a test subject for a couple studies and helped build a bee tunnel. Plus I wrote some code for graphing and analyzing the EAG of the moth experiment as well as some odd functions here and there. Not bad for a recent high school graduate!
I can’t believe it’s all over now. It was a wonderful way to spend a summer. Thank you to everyone who made it possible, especially Greg Gage, Sanja Gage, Etienne Serbe, and Stanislav Mircic. A special thanks to all of the 2018 fellows!
Is It Actually My Choice To Not Title This Post?
Looking doubtful.
Since last I wrote about the “Free Will” project, I have increased the volume of data I have to work with and I have organized it into an intuitive MATLAB database for efficient manipulation via a set of functions for monte carlo analysis, spectrogram generation, etc. I will be making this code available to the public soon. Additionally, the readiness potential was seen when the experiment was conducted with eyes open and fixed, rather than just when eyes were closed. Noise from eye blinks and movements proved to not corrupt the EEG signal enormously.
I also noticed that within the eyes-closed paradigm, a sharp increase in a small frequency band within the alpha range appeared to occur just after movement completion and sometimes up to a short time before the next movement was initiated. (Shown below) Such a discovery may be indicative that mu rhythms are being recorded, which are more thoroughly explained in Anusha’s introductory post. If this rhythm can be tracked in a paradigm of spontaneous movements with no timer or mental counting, it is possible that the disappearance of the mu rhythm could be utilized as one of the characteristic predictors of movement onset. To see these rhythms most clearly, I filtered the data between 5 and 15 Hz. To smooth out the readiness potential most clearly, an aggressive filter between 0.01 and 5 Hz is used. In applying both filters to the same dataset separately, I was able to find the readiness potential as well as what might be mu rhythms.
I’ve constructed an experiment which incorporates a wooden clock (shown below) whose revolutions can be tracked through the BYB software. This will allow people to see when their average decision was made with respect to movement initiation along the course of the readiness potential. A DC motor holding the clock hand is powered by a battery and has a speed which can be adjusted via a series potentiometer. A photoresistor is then placed under a hole in the clock located just beneath the 12. The photoresistor is supplied power via a USB cord connected to the computer. Upon every revolution, the hand of the clock blocks the light from entering the opening and a change in voltage can be interpreted by the spike recorder. Best results were achieved when a light was placed over the clock for greater contrast. The clock works smoothly and in the experiment, the participant is instructed to self-report decision time after completion of the movement. The position on the clock and subsequently the point in the recording can be found from this information. Unfortunately, a consistent issue encountered in this experiment is that the self-reported times tend to fall a short time after movement initiation (not possible). Thus, I will need to adjust the paradigm for more accurate results. A histogram of relative decision times is shown below, where positive values indicate a decision falling after movement initiation.
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Moving forward, I plan to attach an accelerometer to the head as a means of comparing the readiness potential signal to the movement artifacts which arise from wrist flexion. This will allow for the presence of the readiness potential to be properly validated, discarding the possibility of erroneous results due to recording electrode motion. I am still searching for more predictors of motion across other brain regions including premotor and prefrontal areas, though the readiness potential and “mu rhythms” are a huge step in the right direction.
I didn’t write a lot of blog posts this summer because I didn’t have my own research project, but the other research projects kept me plenty busy. I converted over an old BYB library written in a pricy programming language called Matlab into a free open source language called Python. I also cleaned it up and commented out all of the code while I was at it.
