Comparison of Motor Imagery EEG Classification using Feedforward and Convolutional Neural Network
- Resource Type
- Authors
- Stefan Oniga; T. Majoros
- Source
- IEEE EUROCON 2021 - 19th International Conference on Smart Technologies.
- Subject
- Artificial neural network
medicine.diagnostic_test
business.industry
Computer science
Interface (computing)
Pattern recognition
Electroencephalography
Convolutional neural network
Motor imagery
medicine
Feedforward neural network
Artificial intelligence
Data pre-processing
business
Brain–computer interface
- Language
Brain-computer interface (BCI) is widely used in several clinical applications. Motor imagery-based BCI can help patients who have lost their motor functions in communication and rehabilitation. To develop such BCI applications, the accurate classification of motor-imagery based electroencephalography (EEG) is crucial. By processing a publicly available EEG dataset, we obtained information that can be used to train neural networks and efficiently classify activities performed by volunteers. In this paper we used several data pre-processing methods and examined how they affect the classification performance of a feedforward neural network. As the results were not satisfactory with the feedforward network, the data prepared with the best pre-processing method were also used to train a convolutional neural network (CNN). We achieved an accuracy of 91.27% in classifying fists and feet closing activities using data from ten volunteers.