  This research identifies a gap in weakly-labelled multivariate time-series
classification (TSC), where state-of-the-art TSC models do not per-form well.
Weakly labelled time-series are time-series containing noise and significant
redundancies. In response to this gap, this paper proposes an approach of
exploiting context relevance of subsequences from previous subsequences to
improve classification accuracy. To achieve this, state-of-the-art Attention
algorithms are experimented in combination with the top CNN models for TSC (FCN
and ResNet), in an CNN-LSTM architecture. Attention is a popular strategy for
context extraction with exceptional performance in modern sequence-to-sequence
tasks. This paper shows how attention algorithms can be used for improved
weakly labelledTSC by evaluating models on a multivariate EEG time-series
dataset obtained using a commercial Emotiv headsets from participants
performing various activities while driving. These time-series are segmented
into sub-sequences and labelled to allow supervised TSC.
