  Classifying limb movements using brain activity is an important task in
Brain-computer Interfaces (BCI) that has been successfully used in multiple
application domains, ranging from human-computer interaction to medical and
biomedical applications. This paper proposes a novel solution for
classification of left/right hand movement by exploiting a Long Short-Term
Memory (LSTM) network with attention mechanism to learn the
electroencephalogram (EEG) time-series information. To this end, a wide range
of time and frequency domain features are extracted from the EEG signals and
used to train an LSTM network to perform the classification task. We conduct
extensive experiments with the EEG Movement dataset and show that our proposed
solution our method achieves improvements over several benchmarks and
state-of-the-art methods in both intra-subject and cross-subject validation
schemes. Moreover, we utilize the proposed framework to analyze the information
as received by the sensors and monitor the activated regions of the brain by
tracking EEG topography throughout the experiments.
