Improving the common spatial pattern filtering method to improve the efficiency of brain computer interface systems

Master's thesis in the field

Medical Engineering

Abstract

Improvement of joint spatial pattern filtering method to improve the efficiency of computer-brain interface systems

       Brain-computer interface systems are systems that can translate brain electrical signals associated with movement ideas in the human brain into comprehensible computer commands. Therefore, this capability can help many sensory-motor patients and solve their problems in relation to their surroundings to a very high extent. However, the current systems have not yet been able to enter the market commercially due to the lack of high enough accuracy for all people.

In order to improve the accuracy and performance of these systems, it has been experimentally shown that the use of the "joint spatial pattern" method for extracting suitable separating features from brain signals is an optimal method in synchronous computer-brain interface systems, but it still has problems and challenges.  In this research, two solutions have been proposed to modify the non-linear method of the joint spatial pattern: the first solution, which is the personalization of the linear dust algorithm, includes two new combined methods. The first method, by adding signal frequency-spatial information to the common spatial pattern method, has led to the improvement of the algorithm's performance in order to find better distinctive features, and based on these features, the accuracy of data classification increases. In the second method, which is implemented in two different ways, the coactivity value of all channels is injected two by two into the CSP formulation, and then the values ??obtained by a linear kernel function go to another space and finally improve the separation between motion perceptions.

The second solution is to adapt the algorithm to the new data entry, which has been done once by defining a kernel function with a large number of free parameters and once again by updating the matrix Kernel covariance has been formed, which according to the obtained results, this solution is associated with problems in both ways and could not provide higher diagnosis accuracy. Therefore, there should be some corrections on them in the future works.

Human brain and its activities

From the point of view of anatomy and physiology, the nervous system and especially the human brain is known as the main processor, decision maker and controller of all human behaviors. It can be said that the brain system is the most complex and unknown organ of the human body because the way it functions in topics such as learning, memory, information processing, creating relationships between different concepts, etc., has not been clearly defined yet, and there are many questions for the scientists of the world. Knowing more and more how the different parts of the brain work can be effective in solving many neurological, psychological, sensory and motor deficiencies of the human body, and this fact has made scientists pay more and more attention to exploring how the human brain works in recent years.

In order to process brain signals, a way to receive them must first be found. Neurons are the smallest data processing unit in the brain, and electrical communication between them is the basis of brain function [1]. The electrical connection between neurons creates electric and magnetic fields and, as a result, electric currents in the volume of the brain, and measuring these fields and currents is one of the ways to record brain activities. Among the methods used in this field, EEG [1] can be mentioned, in its non-invasive type, a set of electrodes recording the electrical signal is placed on the scalp and the electrical signals of the brain are recorded while performing a specific activity. In another method called magnetoencephalography or MEG [2], the magnetic activities of the brain are recorded by means of superconducting recorders placed on the head. But basically, the MEG recording device is very expensive and has high technology. In addition to the methods of recording electrical activities in the brain, there are other methods that record metabolic activities in the brain. In fact, these methods are different medical imaging methods such as fMRI, PET, SPECT, etc.They use and determine which parts of the brain have metabolic activities at any given moment. The advantages of brain metabolic imaging methods include high spatial resolution and the possibility of three-dimensional localization of brain activity sources. But most of these methods are very expensive and not always available.

brain computer interface systems

How to establish communication between today's smart artifacts and the human brain is one of the challenges of medical engineering knowledge. And brain-computer interface systems or [1]BCI have been developed in order to solve this challenge. In fact, they are systems that have the ability to separate brain images (especially the image of body movements) and translate them into commands that can be understood by the computer. Based on the individual's brain signals, this capability can help patients such as those suffering from cerebral-spinal insufficiency such as ALS[2] or other motor insufficiency[3] [2,3]. Because the BCI system provides a communication channel for this type of patients that does not depend on peripheral nerves and muscles. For example, in ALS disease, the neurons related to voluntary movement degenerate over time and the person gradually loses the ability to move his various organs. In the initial stages of this disease, problems arise in moving the hands and feet, chewing and swallowing food, and in the final stages, the patient completely loses the ability to walk and use his hands and feet, and the act of chewing and swallowing becomes very difficult for him, so that the patient may suffocate. Also, at this stage, even the person's breathing faces problems and the person will not be able to speak [4].

Although in ALS, the person loses the ability to move his limbs, this disease does not affect the sensory capabilities (hearing, smell, sight, taste, and touch) as well as the mental activities of the person, and the patient has the ability to imagine performing various movements. Therefore, if with the help of the BCI system it is possible to determine from the brain signals what kind of movement the patient imagined, he can be given great help. In this way, such patients, as well as other reasonable patients (who are intellectually healthy) who have motor impairment, can control prostheses and artificial organs and external devices such as light switches, etc. by motor imagination (MI) [4], which greatly expands the personal ability of a person. Research has shown that when a member of a procedure [5] (such as the left hand) is moved, the range of Mu Rhythm (9-13Hz) and Beta Rhythm (18-22Hz) of the motor cortex of the other side of the brain [6] is significantly reduced, while the rhythm of the motor cortex of the same side of the brain [7] is significantly increased. Furthermore, when a person imagines performing an action (without actually performing the action), they activate the same motor area of ??the brain (and produce the same EEG pattern) as when they actually perform the action [5]. In fact, the performance of computer-brain interface devices is based on this issue and considers the signals of the perception of making a movement for examination. Systems

BY

Tannaz hadiyan

    Brain Computer Interface (BCI) systems try to translate EEG changes which result from motor imagery, into the form of cursor or object movement on the screen. This ability can be worked for the sensory and motor neuropathy patients and help them communicate with their environment