Classification of EEG Motor Imagery Tasks Using Convolution Neural Networks
- Publication Type:
- Conference Proceeding
- Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2019, 2019 pp. 758 - 761
- Issue Date:
|Classification+of+EEG+using+Deep+Learning+Final+Version+v1.3-HM.pdf||Accepted manuscript||595.61 kB|
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is currently unavailable due to the publisher's embargo.
The embargo period expires on 31 Jul 2021
Electroencephalograph (EEG) is a highly nonlinear data and very difficult to be classified. The EEG signal is commonly used in the area of Brain-Computer Interface (BCI). The signal can be used as an operative command for directional movements for a powered wheelchair to assist people with disability in performing the daily activity.In this paper, we aim to classify Electroencephalograph EEG signals extracted from subjects which had been trained to perform four Motoric Imagery (MI) tasks for two classes. The classification will be processed via a Convolutional Neural Network (CNN) utilising all 22 electrodes based on 10-20 system placement. The EEG datasets will be transformed into scaleogram using Continuous Wavelet Transform (CWT) method.We evaluated two different types of image configuration, i.e. layered and stacked input datasets. Our procedure starts from denoising the EEG signals, employing Bump CWT from 8-32 Hz brain wave. Our CNN architecture is based on the Visual Geometry Group (VGG-16) network. Our results show that layered image dataset yields a high accuracy with an average of 68.33% for two classes classification.
Please use this identifier to cite or link to this item: