Classification of MI EEG signal using an advanced Deep Learning architecture for a Lower-limb rehabilitation exoskeleton
| dc.contributor.author | Titkanlou, Maryam Khoshkhooy | |
| dc.date.accessioned | 2025-11-12T10:17:52Z | |
| dc.date.available | 2025-11-12T10:17:52Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract-translated | Brain-computer interface (BCI) systems employ motor imagery (MI) electroencephalogram (EEG) signals to control assistive devices like rehabilitative exoskeletons. To enhance MI EEG classification accuracy, this study suggests a novel structure utilizing an advanced deep learning architecture. Our laboratory dataset and an available public dataset will be utilized for training our model. Then Adaptive preprocessing procedures, effective classification approaches, and creative feature extraction techniques will be used to solve important challenges such as non-stationary signals, inter-subject variability, and real-time implementation constraints. Metrics like accuracy, latency, and robustness will be employed to assess the framework’s performance and make sure it is suitable for real-world applications. The goal of this research is developing a generalized deep learning network to improve the classification accuracy of MI EEG signals to assist in the creation of practical, efficient treatments for people with lower limb mobility problems. | en |
| dc.description.sponsorship | This work was supported by Grant No. SGS-2022-016 Advanced methods of data processing and analysis. | en |
| dc.format | 58 s. | cs |
| dc.identifier.uri | http://hdl.handle.net/11025/63850 | |
| dc.language.iso | en | en |
| dc.publisher | University of West Bohemia | en |
| dc.rights | © University of West Bohemia | en |
| dc.subject | elektroencefalogram | cs |
| dc.subject | exoskelet dolních končetin | cs |
| dc.subject | rehabilitace | cs |
| dc.subject | hluboké učení | cs |
| dc.subject.translated | electroencephalogram | en |
| dc.subject.translated | lower-limb exoskeleton | en |
| dc.subject.translated | rehabilitation | en |
| dc.subject.translated | deep learning | en |
| dc.title | Classification of MI EEG signal using an advanced Deep Learning architecture for a Lower-limb rehabilitation exoskeleton | en |
| dc.type | report | en |
| dc.type | zpráva | cs |
| local.files.count | 1 | * |
| local.files.size | 785546 | * |
| local.has.files | yes | * |
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