Electroencephalography based brain controlled grasp and lift
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
| Main Authors: | , , , |
|---|---|
| Outros Autores: | |
| Formato: | Thesis |
| Idioma: | English |
| Publicado em: |
Brac University
2022
|
| Assuntos: | |
| Acesso em linha: | http://hdl.handle.net/10361/16587 |
| id |
10361-16587 |
|---|---|
| record_format |
dspace |
| spelling |
10361-165872022-05-11T21:01:39Z Electroencephalography based brain controlled grasp and lift Chakraborty, Pritam Akter, Sanjida Hasin, Bariyat Ahmed, Syeda Faria Parvez, Mohammad Zavid Rahman, Rafeed Department of Computer Science and Engineering, Brac University BCI Electroencephalography Brain wave Memory cell Signal processing Neurosciences Signal processing -- Digital techniques -- Computer programs. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 40-42). The complexities and challenges of performing daily fundamental activities like getting dressed, answering a phone call, opening and closing the door, writing something down, or even consuming foods for patients who have lost their functionality of hands and arms due to neurological disability or amputations, is something anyone could never imagine. Our research paper serves to show the importance of restoring patients’ ability to do daily activities to increase their mobility and standard of living. In this paper, we have proposed an innovative, resilient and dynamic implementation of a grasp and lift technology that would accumulate brain signals in the form of waves to operate prosthetic limbs without the help of an external device and wires. We have decided to use Electroencephalography to reactivate the neuromuscular bypass with the help of an EEG device for obtaining brain signals that correspond to specific circumstances from the scalp surface area. We also have established models using Neural Networks that would monitor multimodal sensory activities which include object encounter, grasp, lift-off, replacement from the dataset and assist the users of this technology to operate the prostheses only by incorporating their brain signals. The artistry of the whole procedure incorporates substantial segments like signal acquisition and pre-processing of the signals into data, feature extraction, denoising etc. which later leads us to implement CNN and LSTM models. After implementing the models we obtained the accuracy of 90.11% and 74.44% from the CNN and LSTM model respectively. Throughout the implementation, there will be a differential boost in the accuracy level for each of the models. Therefore, our paper is an evidence of how EEG is considered to be a communication channel between prosthetic devices and the human brain. Furthermore, it intricately reveals the approach of grasp and lift technology through signal acquisition, processing, and implementation based on Electroencephalography. Pritam Chakraborty Sanjida Akter Bariyat Hasin Syeda Faria Ahmed B. Computer Science 2022-05-11T04:03:39Z 2022-05-11T04:03:39Z 2021 2021-09 Thesis ID 18101357 ID 18101215 ID 17101300 ID 18101161 http://hdl.handle.net/10361/16587 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 42 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
BCI Electroencephalography Brain wave Memory cell Signal processing Neurosciences Signal processing -- Digital techniques -- Computer programs. |
| spellingShingle |
BCI Electroencephalography Brain wave Memory cell Signal processing Neurosciences Signal processing -- Digital techniques -- Computer programs. Chakraborty, Pritam Akter, Sanjida Hasin, Bariyat Ahmed, Syeda Faria Electroencephalography based brain controlled grasp and lift |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Parvez, Mohammad Zavid |
| author_facet |
Parvez, Mohammad Zavid Chakraborty, Pritam Akter, Sanjida Hasin, Bariyat Ahmed, Syeda Faria |
| format |
Thesis |
| author |
Chakraborty, Pritam Akter, Sanjida Hasin, Bariyat Ahmed, Syeda Faria |
| author_sort |
Chakraborty, Pritam |
| title |
Electroencephalography based brain controlled grasp and lift |
| title_short |
Electroencephalography based brain controlled grasp and lift |
| title_full |
Electroencephalography based brain controlled grasp and lift |
| title_fullStr |
Electroencephalography based brain controlled grasp and lift |
| title_full_unstemmed |
Electroencephalography based brain controlled grasp and lift |
| title_sort |
electroencephalography based brain controlled grasp and lift |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/16587 |
| work_keys_str_mv |
AT chakrabortypritam electroencephalographybasedbraincontrolledgraspandlift AT aktersanjida electroencephalographybasedbraincontrolledgraspandlift AT hasinbariyat electroencephalographybasedbraincontrolledgraspandlift AT ahmedsyedafaria electroencephalographybasedbraincontrolledgraspandlift |
| _version_ |
1814307932694642688 |