A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional Control
An efficient recognition model is highly recommended while trying to analyze brain signal pattern for Motor Imagery (MI) signal. Therefore, this study aims to develop an optimized model based on a deep learning approach using Multi-Layer Perceptron (MLP) in order to help a large community of disabi...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tikrit University
2022-12-01
|
| Series: | Tikrit Journal of Pure Science |
| Subjects: | |
| Online Access: | https://tjpsj.org/index.php/tjps/article/view/107 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849427030665330688 |
|---|---|
| author | Zaid Raad Saber Zubair |
| author_facet | Zaid Raad Saber Zubair |
| author_sort | Zaid Raad Saber Zubair |
| collection | DOAJ |
| description |
An efficient recognition model is highly recommended while trying to analyze brain signal pattern for Motor Imagery (MI) signal. Therefore, this study aims to develop an optimized model based on a deep learning approach using Multi-Layer Perceptron (MLP) in order to help a large community of disability people by allowing them to control the wheelchair using their MI Brain signal. In this paper, dataset is used which is belong to BCI Competition dataset IV/2b and consists of two parts, each of them contains on 160 trails for a single subject. To preprocess the brain signal, Butterworth band pass filter used to remove unwanted signal (Alpha and Beta) and remain on the brain signal, then followed by feature extraction technique using Discrete Wavelet Transform (DWT). After that, Multi-Layer Perceptron (MLP) classifier based training parameters utilized to optimize the performance of the proposed system through using grid search optimization to improve performance of distinguishing between the two directional wheelchair commands. Cross-validations with ten groups were adopted to boost the modeling accuracy with dataset of all subjects (1440 trials) and the single subjects (160 trails). The results of this study showed that the efficiency of the optimized MLP model increased by 3% over the large dataset compared to the non-optimized model. It can be concluded that the optimized model can be deployed in a MI based BCI wheelchair control system to help the disability people in their daily activities.
|
| format | Article |
| id | doaj-art-812c0a1a2b114cd7837bc210d943b554 |
| institution | Kabale University |
| issn | 1813-1662 2415-1726 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Tikrit University |
| record_format | Article |
| series | Tikrit Journal of Pure Science |
| spelling | doaj-art-812c0a1a2b114cd7837bc210d943b5542025-08-20T03:29:09ZengTikrit UniversityTikrit Journal of Pure Science1813-16622415-17262022-12-0126110.25130/tjps.v26i1.107A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional ControlZaid Raad Saber Zubair An efficient recognition model is highly recommended while trying to analyze brain signal pattern for Motor Imagery (MI) signal. Therefore, this study aims to develop an optimized model based on a deep learning approach using Multi-Layer Perceptron (MLP) in order to help a large community of disability people by allowing them to control the wheelchair using their MI Brain signal. In this paper, dataset is used which is belong to BCI Competition dataset IV/2b and consists of two parts, each of them contains on 160 trails for a single subject. To preprocess the brain signal, Butterworth band pass filter used to remove unwanted signal (Alpha and Beta) and remain on the brain signal, then followed by feature extraction technique using Discrete Wavelet Transform (DWT). After that, Multi-Layer Perceptron (MLP) classifier based training parameters utilized to optimize the performance of the proposed system through using grid search optimization to improve performance of distinguishing between the two directional wheelchair commands. Cross-validations with ten groups were adopted to boost the modeling accuracy with dataset of all subjects (1440 trials) and the single subjects (160 trails). The results of this study showed that the efficiency of the optimized MLP model increased by 3% over the large dataset compared to the non-optimized model. It can be concluded that the optimized model can be deployed in a MI based BCI wheelchair control system to help the disability people in their daily activities. https://tjpsj.org/index.php/tjps/article/view/107Brain - Computer InterfaceMotor ImageryWheelchair ControlOptimizationMLPDeep Learning |
| spellingShingle | Zaid Raad Saber Zubair A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional Control Tikrit Journal of Pure Science Brain - Computer Interface Motor Imagery Wheelchair Control Optimization MLP Deep Learning |
| title | A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional Control |
| title_full | A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional Control |
| title_fullStr | A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional Control |
| title_full_unstemmed | A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional Control |
| title_short | A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional Control |
| title_sort | deep learning based optimization model for based computer interface of wheelchair directional control |
| topic | Brain - Computer Interface Motor Imagery Wheelchair Control Optimization MLP Deep Learning |
| url | https://tjpsj.org/index.php/tjps/article/view/107 |
| work_keys_str_mv | AT zaidraadsaberzubair adeeplearningbasedoptimizationmodelforbasedcomputerinterfaceofwheelchairdirectionalcontrol AT zaidraadsaberzubair deeplearningbasedoptimizationmodelforbasedcomputerinterfaceofwheelchairdirectionalcontrol |