Is It Actually My Choice To Not Title This Post?

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.

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.

Creating Professor X

Remember Professor Charles Francis Xavier? The founder and leader of X-men has phenomenal telepathic abilities. But, alas, he only exists in fiction! Or so we thought. What if we had the technology to make a part of Professor X’s abilities reality? We could channel the superpower of looking into people’s minds to know what movement they’re thinking of, before they make it. Maybe translate that tech into a robotic arm or leg?  I am working on making it reality this summer.

But how do we get around to predicting one’s imagined movements? One approach is to measure the “Mu Rhythms,” or also known as “Mu Waves” from the sensorimotor area of one’s brain by registering an EEG signal. The Mu Waves are associated with the movement of the body – either by actually moving any part of your body or by “thinking” about moving that particular part of your body. The sensorimotor area is a narrow strip that goes from one ear to the other one by along the top of the head. As we can see in the image below, the sensorimotor area involves the ‘Primary motor cortex’ and ‘Primary somatosensory cortex.’

Let’s explore these ‘Mu waves’ a little more. They occur in the above-mentioned regions only when the body is resting, or particularly when this particular cortex of the brain is ‘resting/idling.’ When we move a part of our body, the mu waves corresponding to that region disappear, or in scientific terms, these waves are desynchronized. The desynchronization will occur when the cortex is no more in the resting state. Interestingly, our brain isn’t idling when we’re imagining a movement. Which means, this desynchronization of the mu waves should be visible by mere imagination of a certain movement. And when I say imagining, I do not mean visual imagination, but the actual feeling of it, somewhat like imagining how it would *feel* like to move your hand without actually moving it. At this point, I am still working towards finding these rhythms, but theoretically, they should look somewhat like the highlighted region below (C4 corresponds to the left hand):

The positioning of the electrodes plays an important role in detecting the mu-rhythms! I hope to see these rhythms by the end of this week! Fingers crossed!

What next?

Once we do find these rhythms, the next step would be to quantify the suppression of the mu-waves in order to predict whether the body is relaxed or whether there’s some activity going on (either actual movement or the imagination of it). Once that is accomplished we can go ahead and measure the activity from different regions and predict which body part is the activity associated with.

A little about me!

My name is Anusha, and this is my first year in the US. I am pursuing my Master’s in Electrical and Computer Engineering from University of Michigan, Ann Arbor. Currently, I am basking in the much-awaited and short-lived summer in Ann Arbor; long walks around the sprawling beautiful campus and the lush green arboretum, seeking solace in the sunset by the Huron river with a nice cup of coffee and a good book, losing myself in the world of fiction. Music is one of my getaways; I have been trained in classical Indian music throughout my undergrad in India and take pleasure in singing from time to time. I’m also passionate about cooking, baking and eating of course.  Here’s me with my brother.