Analyzing and Decoding Natural Reach & Grasp Action Using Convolutional Neural Network / (Record no. 609194)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 02291nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Nazir, Abida |
| 245 ## - TITLE STATEMENT | |
| Title | Analyzing and Decoding Natural Reach & Grasp Action Using Convolutional Neural Network / |
| Statement of responsibility, etc. | Abida Nazir |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 44p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Reaching and Grasping is most signi cant component of human life.Translation of EEG in the form of upper limb movement is of great importance for realization of natural neuroprosthesis control and restoration of hand movements of patients with motor disorders. Patients su ering from spinal cord injury (SCI)problems have lost most of voluntary motor control functions. Such type of loss can be cured using movement related cortical potentials (MRCPS) analysis. Brain computer interface with limb neuro-prosthesis is considered as a solution to such problems. This study anlyzes EEG signals in relation with natural reach and grasp actions. EEG signals have movement related cortical potentials (MRCPS) which can be used to decode upper limb movements. This experiment was performed in Graz University of Technology Austria and they o ered free access dataset for further exploration.Total 45 subjects were involved in this study, 15 subjects with every type of electrode:gel,water and dry performed the experiment. All subjects accomplished self-initiated 80 reach and grasp actions toward a spoon within the jar (lateral grasp) and toward an empty glass (palmar grasp).EEG signals are recorded using three types of electrodes: water based, Gel based and Dry electrodes. In this study signals are classi ed using Deep learning technique i.e Convulotional Neural Networks. For analysis, EEG signals were preprocessed using various lteration techniques. After ltration data is fed into classi er for classi cation of signals. Data is divided into test set and training set. Grand average peak accuracy calculated on unseen test data resulted in 54.2% classi cation accuracy i.e Gel based accuracy approached 56.8.4%, water based 52.7% and dry based 51.8%. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Sciences (BMS |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Muhammad Asim Waris |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/30722">http://10.250.8.41:8080/xmlui/handle/123456789/30722</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 05/20/2024 | 610 | SMME-TH-705 | Thesis |