Survey and Investigation of the design framework of Brain-Machine Interfaces used in Neural Prosthetics
| dc.contributor.advisor | Getachew Alemu | |
| dc.contributor.author | Ermias Telahun | |
| dc.date.accessioned | 2023-12-05T06:47:40Z | |
| dc.date.available | 2023-12-05T06:47:40Z | |
| dc.date.issued | 2020-10 | |
| dc.description.abstract | In a world inundated in technology, the line between humans and machines has begun to blur; our thoughts and actions are increasingly shaped and substantiated by machines. Perhaps nowhere is this blurring more evident than in the scientific endeavor of Brain-Machine Interface (BMI; Brain Computer Interface, BCI). This endeavor seeks to use electrical signals generated by action potentials in the nervous system and interface it with a computer or a device so as to regain communication with the outside world as well as motor functions by using an artificial limb. The need for using a BMI is seen most clearly in paralyzed patients who have lost partial (paraplegia) or total (quadriplegia) use of their motor functions, as well as in patients of chronic progressive diseases as Amyotrophic Lateral Sclerosis (ALS). Over the past two decades a vast array of researches have been conducted in BMI Neural Prosthetics. Initially the experiments were performed on rodents. Then the studies developed to using primates in a grasping experiment. In the past couple of years electrodes implanted intracranially into the skull of a quadriplegic person has led to using a robotic arm through though only. Noninvasive BMI researches have also proliferated in the past couple of decades. The current study proposes to do a systematic review on the various studies already performed in BMI Neural Prosthetics in order to investigate and suggest the best approach to design a BMI system. An in-depth explorative survey was conducted to look into the various steps towards development of a complete system design to alleviate existing disabilities. A comparative analysis using the noninvasive EEG device ‘Emotiv EPOC’ was performed to compare the control signal types, feature extraction mechanisms, classification algorithms and their corresponding accuracies and applications. Accordingly, the best design framework of BMIs in Neural Prosthetics was suggested, which is a good addition to the pool of researches in the scientific community. | |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/224 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.title | Survey and Investigation of the design framework of Brain-Machine Interfaces used in Neural Prosthetics | |
| dc.type | Thesis |