Brain-Computer Interface based on Neural Network with Dynamically Evolved for Hand Movement Classification
- Resource Type
- Conference
- Authors
- Sakti, Widhi Winata; Anam, Khairul; Pratama, Mahardhika; Bukhori, Saiful; Hanggara, Faruq Sandi; Liswanto, Budi
- Source
- 2022 FORTEI-International Conference on Electrical Engineering (FORTEI-ICEE) Electrical Engineering (FORTEI-ICEE), 2022 FORTEI-International Conference on. :72-75 Oct, 2022
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Performance evaluation
Adaptation models
Predictive models
Brain modeling
Real-time systems
Electroencephalography
Data models
NADINE
hand movement
- Language
Translating brain commands into movements on the prosthetic robot is not an easy task. It is needed a control system for the prosthetic robot based on human body signals to predict the desired movement so that the robot is part of the body. This assistive device is used to help people with disabilities perform functional movements such as gripping with motor activities performed on all five fingers. This paper proposed a hand movement recognition system based on electroencephalogram (EEG) using the Neural Network with Dynamically Evolved Capacity (NADINE). The data generated from the model test shows almost the same value as NADINE, with a maximum accuracy of 98% and an average prediction time of 14 milliseconds. These results further strengthen that the NADINE model can be used in real-time.