  Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an
important component of the Brain-Computer Interface (BCI) system that helps
motor-disabled people interact with the outside world via external devices. The
main issue in developing the EEG based BCI is the informative confusion due to
the non-stationary characteristics of EEG data. In this work, an innovative
idea of transforming an EEG signal into the weight vector of an unsupervised
neural network called the autoencoder is proposed for the first time to solve
that problem. Separate autoencoders are trained for the individual EEG data.
The weight vectors are then optimized for the individual EEG signals. The EEG
signals are thus represented in a new domain that is in the form of weight
vectors of the individual autoencoder. The weight vectors are then used to
extract features such as autoregressive coefficients (ARs), Shannon entropy
(SE), and wavelet leader. A window-based feature extraction technique is
implemented to capture the local features of the EEG data. Finally, extracted
features are classified using a classifier network. The proposed approach is
tested on two publicly accessible EEG datasets (BCI competition-III and
Competition-IV) to ensure that it is as successful as and superior to the
previously published methods. The proposed technique achieves a mean accuracy
of 95.33 % for dataset-IIIa from BCI-III and a mean accuracy of 97% for
dataset-IIa from BCI-IV for four-class EEG-based MI classification. The
experimental outcomes show that the proposed approach is a promising way to
increase BCI performance.
