Sara Alansari

Bio: Sara Alansari is an academic researcher from Ajman University of Science and Technology. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

EEG-based communication system for patients with locked-in syndrome using fuzzy logic

Abstract: Patients who are conscious and aware of their environment but are physically disabled are known to have Locked-in Syndrome. The causes for this medical condition include traffic accidents, drug addiction and brain clots. There are some available solutions nowadays to help them communicate but the down side is the requirement for physical training which can be both time and money consuming. The main objective of this project is to help these patients communicate and engage more effectively in their daily life. In this paper, an Electroencephalogram (EEG)-based communication system is developed to facilitate communication of these patients with their caretakers. The implementation is composed of both hardware and software. The hardware consists of a 14-channels EEG module and a tablet. The software parts are: processing algorithm, online database and an android application. The EEG module on the patients' scalps keeps reading brainwaves continuously. Meanwhile, the tablet in front of them displays six basic needs, namely, food, water, washroom, help, sleep and entrainment. When the patients focus on a specific need, it will be detected when it matches with a predefined reference in the processing algorithm. The processing is done using fuzzy logic pattern recognition based on eye movement and color detection. The database acts as a two-way communication link between the patients and their caretaker. As the message will be sent through it to the android application-which is installed in the caretakers' phones-in the form of a pop-up notification. Interchangeably, a response message can be sent by the caretakers to state they are on their way for instance. Besides, the tablet will generate a voice message to inform the people around the patients about their need. A prototype system has been developed and successfully tested.

The electroencephalogram (EEG).

Abstract: The treatment of every epilepsy patient is highly individualized and strictly under medical supervision. The choice of the drug, the dosage, total duration of treatment after the last attack and mode of withdrawal of drugs are highly individualized. As the cost of drugs like Carbamazepine and Sodium Valproate is high and since the duration of the treatment is in years, economic constraints become an important factor for the choice of drug.

Intelligent Exoskeleton for Patients with Paralysis

Abstract: The topic of paralysis has gained a lot of interest among scientist over the last years. Therefore, many projects were made for patients suffering from paralysis, yet none has succeeded in achieving an effective way to give these patients the ability to control their paralyzed body parts. This paper proposes a robotic upper-limb exoskeleton guided using 3D object detection and recognition and controlled via Electroencephalogram (EEG) signals. The proposed system is dedicated to patients with complete monoplegic, hemiplegic and quadriplegic paralysis. The main objective of the system is to give these patients the ability to move their upper-limb, and control a wheelchair using their thoughts, which offers them independence, better life quality and assist them in leading active roles in the society. This system consists of four main components, namely, EEG module, infrared (IR) depth camera, 3D printed upper-limb exoskeleton and a motorized wheelchair. The former two are used as inputs to the system and the collaboration between them shows the uniqueness of the proposed approach. EEG signals are segmented and classified through Fuzzy Logic technique and the results are used for choosing the desired object for grabbing from the surface of a table. Movement to the desired object is executed based on the 3D coordinates obtained from the IR depth camera, while inertial measurement unit (IMU) sensor is placed on the arm as a feedback element to ensure accurate movement and proper safety measures. System prototype showed sufficient results for the proposed idea.

Distinguishing anger in the brain using machine learning on EEG data

Abstract: Monastic debate is a form of meditation aimed at enhancing emotional regulation. It is an integral part of monastic training and serves as a complementary practice that promotes beneficial emotions and minimizes destructive ones in the process. This study attempted at attesting the validity of this form of meditation by examining, through the use of EEG, whether monks that are experienced in monastic debate have a lower tendency of becoming angry than inexperienced monks. The expectation is that compared to beginner monks, experienced monks would have benefited more from this type of training, enhancing their ability to regulate emotions and thus exhibit a lower number of occurrences of anger. Comparing moments of anger and non-anger, we found a significant difference in terms of oscillatory power in alpha, beta, and theta frequency bands across multiple electrodes that could potentially distinguish anger. In order to be able to differentiate between anger and non anger moments on a single-trial level, three support-vector machines with different kernels and a K-Nearest-Neighbour classifier with grid search optimization were built to predict occurrences of anger using two sets of feature vectors: i) discovered by our statistical analysis ii) retrieved from literature. The best accuracies obtained were 54.52% (SVM) and 80.61% (KNN). Experienced monks were found to have shorter and less frequent anger episodes (three anger moments that lasted four seconds on average), compared to inexperienced monks (13 anger moments that lasted 7.3 seconds on average), suggesting the fact that this form of training may be able to help regulate anger. The poor accuracy scores can be improved by collecting and making use of more data, and applying different validation techniques to reduce noise.

Use of linguistic signified to support information communication

Abstract: Introduction:Ferdinand Saussure, linguist, semiologist, philosopher and one of the main founders of semiotics, affirms that "Meaning"(significance) is a representation of something created in the mind, an association that is useful for a "Signifier" that is a psychic impression of sound. Objective:In this context, the objective of this research is to verify the feasibility of generating communication based on images formed in the mind (Signified) and what it can represent cognitively related to the Signifier using a brain-computer interface Method:A computer brain interface has been developed and a user has been tested so that it uses neuro-muscular commands and pure mental commands that invoke Signified (records in the user's mind) that represent a goal of communicating. Result:The results allow to evaluate a relationship between signified and signifier of information drawing from thebrain. Psychic images of a communication intent were linked to sound images that are also mental entities, when brain activated, are converted in “speech” (physical sound) computationally. Conclusion:The results demonstrate the feasibility of communication in this modality, which could support the basic communication needs of people who do not communicate orally