Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers
Main Article Content
Abstract
Electroencephalogram (EEG)signals based brain-computer interfacing (BCI) is the current technology trends in the field of rehabilitation robotic. This study compared the performance of support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) classifier with the combination of eight different features as a feature vector. EEG data were acquired from 20 healthy human subjects with predefined protocols. After the EEG signals acquisition, it was pre-processed followed by feature extraction and classification by using SVM MLP and LDA classifiers. The results exhibited that the SVM method was the best approach with 98.8% classification accuracy followed by MLP classifier. Finally, the SVM classifier and Arduino Mega controller was employed for offline controlling of the gripper of the robotic arm prototype. The finding of this study may be useful for online controlling as well as multi-degree of freedom with multi-class EEG dataset.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.