  Decoding EEG signals of different mental states is a challenging task for
brain-computer interfaces (BCIs) due to nonstationarity of perceptual decision
processes. This paper presents a novel boosted convolutional neural networks
(ConvNets) decoding scheme for motor imagery (MI) EEG signals assisted by the
multiwavelet-based time-frequency (TF) causality analysis. Specifically,
multiwavelet basis functions are first combined with Geweke spectral measure to
obtain high-resolution TF-conditional Granger causality (CGC) representations,
where a regularized orthogonal forward regression (ROFR) algorithm is adopted
to detect a parsimonious model with good generalization performance. The
causality images for network input preserving time, frequency and location
information of connectivity are then designed based on the TF-CGC distributions
of alpha band multichannel EEG signals. Further constructed boosted ConvNets by
using spatio-temporal convolutions as well as advances in deep learning
including cropping and boosting methods, to extract discriminative causality
features and classify MI tasks. Our proposed approach outperforms the
competition winner algorithm with 12.15% increase in average accuracy and
74.02% decrease in associated inter subject standard deviation for the same
binary classification on BCI competition-IV dataset-IIa. Experiment results
indicate that the boosted ConvNets with causality images works well in decoding
MI-EEG signals and provides a promising framework for developing MI-BCI
systems.
