| Type of Material: | Thesis |
| Title: | Brain waves to motor imagery tasks classification using machine learning approaches |
| Researcher: | Tiwari, Smita |
| Guide: | Goel, Shivani | Bhardwaj, Arpit |
| Department: | School of Computer Science Engineering and Technology |
| Publisher: | Bennett University |
| Place: | Greater Noida |
| Year: | 2023 |
| Language: | English |
| Subject: | Computer Science | Computer Science Information Systems | Engineering and Technology | machine learning | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; School of Computer Science Engineering and Technology, Bennett University, Greater Noida; 2023 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-14 12:06:17 | |
| 008 | __ | 261014t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464149 |
| 040 | __ | |dIN-AhILN|aBENN_201310 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aTiwari, Smita |
| 110 | __ | |aSchool of Computer Science Engineering and Technology|0U-0890|dGreater Noida|bBennett University|ein |
| 245 | __ | |aBrain waves to motor imagery tasks classification using machine learning approaches |
| 260 | __ | |aGreater Noida|c2023|bBennett University |
| 300 | __ | |axvii; 167p.|dDVD |
| 502 | __ | |cSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida|d2023|bPhD |
| 518 | __ | |oDate of Award|d2023 |
| 518 | __ | |d2017|oDate of Registration |
| 518 | __ | |d2023|oDate of Completion |
| 520 | __ | |aBrain Computer Interfaces (BCI) provides an interface between external devices such as computers for the reading of the human mind, remote communication, and interaction with disabled and paralyzed people. Electroencephalogram (EEG) signals are used for BCI systems. Motor Imagery (MI) based BCI system allows a person to perform motor movements by having the imagination of the movement of the body without performing any movement of the limb or any external stimulus. The person s intention is translated via the imagination of the motor movement through the brain using EEG signals. An efficient way of communication is required to be designed for speech-impaired persons. So, EEG-based vowel classification is one of the motivations in this direction. The inspiration for EEG-based digit classification is the analysis of EEG signal classification in a complex scenario containing more than five classes of EEG signals with a very large number of its instances using the proposed model |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Information Systems |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |amachine learning |
| 700 | __ | |aGoel, Shivani|eGuide |
| 700 | __ | |aBhardwaj, Arpit|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/515230|yShodhganga |
| 905 | __ | |afromsg |
User Feedback Comes Under This section.