EEG Band Separation Using Multilayer Perceptron for Efficient Feature Extraction and Perfect BCI Paradigm
- Resource Type
- Conference
- Authors
- Sunny, Md. Samiul Haque; Afroze, Nashrah; Hossain, Eklas
- Source
- 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Computing, Communication and Electronics (ETCCE), 2020 Emerging Technology in. :1-6 Dec, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Brain
Wearable computers
Multilayer perceptrons
Feature extraction
Electroencephalography
Matlab
Diseases
EEG
BCI
B-Alert X10
MLP
EEG band separation
- Language
For treatment of mental and brain diseases and diagnosis of abnormalities electroencephalogram (EEG) is an important measurement of brain activity. Feature extraction is vital in brain-computer interface (BCI) in the zone of biomedical and bioinformatics research alongside developing and adopting advanced signal processing techniques. Nonstationary and the nonlinear behavior of the EEG signal is the main challenge in feature extraction process. For the betterment of healthcare services, effective and affordable interpretation methods are the emerging keys. In this paper, the main focus is to separate different frequency band from EEG signal to extract features more efficiently using Multilayer Perceptron (MLP). B-Alert X10 is used for EEG acquisition and for analyzing the signal data, a virtual platform MATLAB has been used. For the classification of EEG bands Multilayer Perceptron Neural Network has been implemented which has been proved to be a more effective method with 95.47% accuracy for the classification.