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Decoding EEG signals of different mental states is a challenging task forbrain-computer interfaces (BCIs) due to nonstationarity of perceptual decisionprocesses. This paper presents a novel boosted convolutional neural networks(ConvNets) decoding scheme for motor imagery (MI) EEG signals assisted by themultiwavelet-based time-frequency (TF) causality analysis. Specifically,multiwavelet basis functions are first combined with Geweke spectral measure toobtain high-resolution TF-conditional Granger causality (CGC) representations,where a regularized orthogonal forward regression (ROFR) algorithm is adoptedto detect a parsimonious model with good generalization performance. Thecausality images for network input preserving time, frequency and locationinformation of connectivity are then designed based on the TF-CGC distributionsof alpha band multichannel EEG signals. Further constructed boosted ConvNets byusing spatio-temporal convolutions as well as advances in deep learningincluding cropping and boosting methods, to extract discriminative causalityfeatures and classify MI tasks. Our proposed approach outperforms thecompetition winner algorithm with 12.15% increase in average accuracy and74.02% decrease in associated inter subject standard deviation for the samebinary classification on BCI competition-IV dataset-IIa. Experiment resultsindicate that the boosted ConvNets with causality images works well in decodingMI-EEG signals and provides a promising framework for developing MI-BCIsystems.

Boosted Convolutional Neural Networks for Motor Imagery EEG Decoding with Multiwavelet-based Time-Frequency Conditional Granger Causality Analysis