Zero Base Training Report

This report will focus on describing the methods used to process and analyze the EEG data. Some preliminary results from the microstate analysis will also be presented.

The examination was done in the lab of Richard Andersen, James G. Boswell Professor of Neuroscience, T&C Chen Brain-Machine Interface Center Leadership Chair, and executive of the T&C Chen Brain-Machine Interface Center. A paper portraying the work shows up in the April 10 issue of the diary eLife.

Epilepsy is a critical neurological disease stemming from temporary abnormal discharges of the brain electrical activity, leading to uncontrollable movements and trembling [1]. Epilepsy is the second most common neurological condition seen in primary practice worldwide with an approximate prevalence of 5.8 per 1000 population in the developed world and between 10.3 per 1000 to 15.4 per 1000 in developing countries [2]. Despite its prevalence, epilepsy can be very challenging to diagnose and treat. People with epilepsy are two or three times more likely to die prematurely when compared to a normal person. Therefore, diagnosing and predicting epileptic seizures accurately appear to be particularly important, which is able to bring more effective prevention and treatment for the patients.

Neuroimaging is also an important mechanism in cognitive psychology. Neuroimaging also known as brain imaging involves “the construction of pictures of the anatomy and functioning of intact brains through such techniques as computerized axial tomography, (CAT, or CT), positron emission tomography (PET), magnetic resonance imaging (MRI) or functional magnetic resonance imaging (fMRI)” (Galotti, 2014).

To do this, they showed a large number of short clips to a subject, who was being measured by an fMRI. The resulting brain activity in each part of the brain was then paired with the clip, creating a database of brain activity to image. When presented with fMRI data, the program attempts to match it with data in its database, then correlates this to an image/clip. The researchers also used a novel model of signal filtering known as motion-energy encoding to create a more accurate interpretation of the data. [2] This work is similar to other fMRI analysis that interpreted fMRI data to predict mood, emotion, and even spatial orientation. [3] Going forward, these techniques could be used to help treat and diagnose psychiatric disorders, as well as many other varied fields such as art, law, and entertainment; additionally, researchers have shown that there is only a small difference between the brain activity while picturing an image and seeing an image. This means that with the appropriate software, it might soon be possible to reconstruct an image from the imagination. [4] While we can’t see dreams yet, we’re on our

The fascination with consciousness dates back to the time of Plato and Descartes. Since those times the term “consciousness” has spurned controversy in many scientific fields, including the fields of biology, psychology, and neuroscience. However, with the recent advancements in brain imaging technologies, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), human consciousness has shifted from being a subjective, abstract idea into being a observable scientific phenomenon. As neuroimaging capabilities progress, the public interest in consciousness also grows.

Dewan reported in 1967 [3] that he and several others could transmit letters of the alphabet using EEG recording; their ability to control voluntarily the amplitude of their alpha waves let them send letters using Morse code. Farwell and Donchin [4] used a second method for transmitting linguistic information by EEG; the P300 response to targets let them determine which of a sequence of displayed letters a person had in mind. Finally, Suppes and colleagues [5,6] reported that they were able to classify EEG responses to heard sentences and that there was information available through EEG concerning imagined speech. The work reported here verifies this latter result and develops it further, with the aim of using EEG brain waves to communicate imagined speech. Classification experiments show that brain-wave signatures of imagined speech lets one distinguish linguistic content, with varying degree of success. These signatures include differences in alpha-, beta- and theta-band activity. Our near-term goal is to use such signatures to design filters that let one distinguish linguistic elements in real time. One application is in further experiments which provide feedback to the thinker as to how recognizable a particular thought is, with the aim of training the thinker to produce brain waves which are more discernible. The focus within is on the results of a single experiment in which subjects produce in their imagination one of two syllables in

Where π1 and π2 are prior probabilities of good and bad credit populations, Q (2∣1) and Q (1∣2) compute the probability of making Type I errors, i.e., a customer with good credit is misclassified as a customer with bad credit, and Type II errors, i.e., a customer with bad credit is misclassified as a customer with good credit, and A (2∣1) as well as A (1∣2) are the corresponding misclassification costs of Type I and Type II errors.

Functional magnetic resonance imaging, or fMRI, is a technology that measures brain activity by detecting changes in blood flow to different parts of the brain. When neurons are activated, or “fire”, they use up the oxygen they have and for a couple seconds afterward receive more oxygen through increased blood flow. fMRI technology measures this change in blood flow, allowing us to indirectly measure brain activity. A scanner is used to take the resting state image, which is the image when the subject tries to relax as much as possible. Another scan is taken when the participant is performing a certain task assigned to him, such as looking at a human face. Then researchers find the difference in blood flow between the two scans. The final image is a conglomeration of the scans of in the neighborhood of a dozen people, not just a single person. The colors on the final image represent the probability that the increased blood flow in a brain region was not due to random chance,

A new area of research, neurofeedback, extends the functionality of EEG as a diagnostic tool into a method of treatment. It is based on the scientific foundation of operant learning, where behavior is increased or decreased based on the consequences of behavior. Low resolution brain electromagnetic tomography (LORETA) is a more advanced form of NF that extends surface EEG NF. It works by using data from 19 channels to localize cortical and subcortical current density, which has the potential to greatly extend the scope and efficacy of NF.

In the mid 20's, Recording mind signals from the human scalp has picked up consideration of humankind. individuals trusted that having a gadget that is equipped for perusing mind flags and changing over them into control and correspondence signs is sci-fi because of films and books such as star trek. Back in the 60's, individuals attempted to begin recording cerebrum flags. However, required innovations for measuring and preparing mind signs were excessively constrained and costly. The primary BCI working framework was found by Dr. Grey Walter in 1964. Dr. Grey found the essential framework of the BCI while curing a patient by seeing that the gadget associated with the patient actuates before the way toward pressing the button by the patient.

Also when a brain is not working properly by experimental or medical stimulation it is possible to see if there is a reduction or improvement in that particular area by measurement or evaluation of performance. The focus of the methodology of

The brain consists of billions of neurones and trillions of connections. Those neurones are able to ‘represent’ things

possible to monitor what the brain looks like, and what it is doing when someone is doing

The existence of such differences suggests the usage of EEG recording as a good diagnostic tool in the assessment of ADD and Learning Disabilities. Lubar does not differentiate between LD and ADD subjects but as the focus of this paper is on treatment of ADD I will limit my dicusssion to the results of ADD subject without imlying anything about effects on LD subjects. On that note, some work was also done in the last decades on using brain activity