A deep learning-based brain-computer interaction system for speech and motor impairment
Abstract Some people may experience accidents, strokes, or diseases that lead to both motor and speech disabilities, making it difficult to communicate with others. Those with paralysis face daily challenges in meeting their basic needs, particularly if they have difficulty speaking. Individuals wit...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-05-01
|
| Series: | Journal of Engineering and Applied Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44147-023-00212-w |
| _version_ | 1797827501781680128 |
|---|---|
| author | Nader A. Rahman Mohamed |
| author_facet | Nader A. Rahman Mohamed |
| author_sort | Nader A. Rahman Mohamed |
| collection | DOAJ |
| description | Abstract Some people may experience accidents, strokes, or diseases that lead to both motor and speech disabilities, making it difficult to communicate with others. Those with paralysis face daily challenges in meeting their basic needs, particularly if they have difficulty speaking. Individuals with dysarthria, amyotrophic lateral sclerosis, and similar conditions may find it challenging to understand speech. The proposed system for automatic recognition of daily basic needs aims to improve the quality of life for individuals suffering from dysarthria and quadriplegic paralysis. The system achieves this by recognizing and analyzing brain signals and converting them to either audible voice commands or texts that can be sent to a healthcare provider's mobile phone based on the system settings. The proposed system uses a convolutional neural network (CNN) model to detect event-related potentials (ERPs) within the EEG signal to select one of six basic daily needs while displaying their images randomly. Ten volunteers participated in this study, contributing to the creation of the dataset used for training, testing, and validation. The proposed approach achieved an accuracy of 78.41%. |
| first_indexed | 2024-04-09T12:49:20Z |
| format | Article |
| id | doaj.art-50f7c419c9b744dabfdc6e9c876beee1 |
| institution | Directory Open Access Journal |
| issn | 1110-1903 2536-9512 |
| language | English |
| last_indexed | 2024-04-09T12:49:20Z |
| publishDate | 2023-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj.art-50f7c419c9b744dabfdc6e9c876beee12023-05-14T11:18:15ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122023-05-0170111810.1186/s44147-023-00212-wA deep learning-based brain-computer interaction system for speech and motor impairmentNader A. Rahman Mohamed0Biomedical Engineering Department, Faculty of Engineering, Misr University for Science and Technology (MUST)Abstract Some people may experience accidents, strokes, or diseases that lead to both motor and speech disabilities, making it difficult to communicate with others. Those with paralysis face daily challenges in meeting their basic needs, particularly if they have difficulty speaking. Individuals with dysarthria, amyotrophic lateral sclerosis, and similar conditions may find it challenging to understand speech. The proposed system for automatic recognition of daily basic needs aims to improve the quality of life for individuals suffering from dysarthria and quadriplegic paralysis. The system achieves this by recognizing and analyzing brain signals and converting them to either audible voice commands or texts that can be sent to a healthcare provider's mobile phone based on the system settings. The proposed system uses a convolutional neural network (CNN) model to detect event-related potentials (ERPs) within the EEG signal to select one of six basic daily needs while displaying their images randomly. Ten volunteers participated in this study, contributing to the creation of the dataset used for training, testing, and validation. The proposed approach achieved an accuracy of 78.41%.https://doi.org/10.1186/s44147-023-00212-wElectroencephalogram (EEG)Brain-Computer Interface (BCI)Event Related Potential (ERP)Convolutional Neural Network (CNN) |
| spellingShingle | Nader A. Rahman Mohamed A deep learning-based brain-computer interaction system for speech and motor impairment Journal of Engineering and Applied Science Electroencephalogram (EEG) Brain-Computer Interface (BCI) Event Related Potential (ERP) Convolutional Neural Network (CNN) |
| title | A deep learning-based brain-computer interaction system for speech and motor impairment |
| title_full | A deep learning-based brain-computer interaction system for speech and motor impairment |
| title_fullStr | A deep learning-based brain-computer interaction system for speech and motor impairment |
| title_full_unstemmed | A deep learning-based brain-computer interaction system for speech and motor impairment |
| title_short | A deep learning-based brain-computer interaction system for speech and motor impairment |
| title_sort | deep learning based brain computer interaction system for speech and motor impairment |
| topic | Electroencephalogram (EEG) Brain-Computer Interface (BCI) Event Related Potential (ERP) Convolutional Neural Network (CNN) |
| url | https://doi.org/10.1186/s44147-023-00212-w |
| work_keys_str_mv | AT naderarahmanmohamed adeeplearningbasedbraincomputerinteractionsystemforspeechandmotorimpairment AT naderarahmanmohamed deeplearningbasedbraincomputerinteractionsystemforspeechandmotorimpairment |