Hand and Leg Movement Prediction using EEG Signal by Stacked Deep Auto Encoder
DOI:
https://doi.org/10.24113/ijoscience.v5i10.230Keywords:
BCI, Non-Invasive, EEG, Feature Extraction, Stacked, Encoder, Deep learning, Accuracy.Abstract
Brain Computer Interface (BCI) is device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. Brain–computer interfacing is an uprising field of research wherever signals extracted from the human brain are used for deciding and generation of control signals. Selection of the most appropriate classifier to find the mental states from electroencephalography (EEG) signal is an open research area due to the signal’s non-stationary and ergodic nature.
In this research work the proposed algorithm is designed to solve an important application in BCI where left hand forward–backward movements and right hand forward-backward movements as well as left leg movement and right leg movement are needed to be classified. Features are extracted from these datasets to classify the type of movements. A staked Deepauto encoder is used for classification of hand and leg movements and compared with other classifiers. The accuracy of stacked deepauto encoder is better with respect to other classifiers in terms of classification of hand and leg movement of EEG signals.
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Copyright (c) 2019 Kulsheet Kaur Virdi, Satish Pawar
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