  Multivariate Time Series Classification (MTSC) is a ubiquitous problem in
science and engineering, particularly in neuroscience, where most data
acquisition modalities involve the simultaneous time-dependent recording of
brain activity in multiple brain regions. In recent years, Random Convolutional
Kernel models such as ROCKET and MiniRocket have emerged as highly effective
time series classification algorithms, capable of achieving state-of-the-art
accuracy results with low computational load. Despite their success, these
types of models face two major challenges when employed in neuroscience: 1)
they struggle to deal with high-dimensional data such as EEG and MEG, and 2)
they are difficult to interpret. In this work, we present a novel ROCKET-based
algorithm, named Detach-Rocket Ensemble, that is specifically designed to
address these two problems in MTSC. Our algorithm leverages pruning to provide
an integrated estimation of channel importance, and ensembles to achieve better
accuracy and provide a label probability. Using a synthetic multivariate time
series classification dataset in which we control the amount of information
carried by each of the channels, we first show that our algorithm is able to
correctly recover the channel importance for classification. Then, using two
real-world datasets, a MEG dataset and an EEG dataset, we show that
Detach-Rocket Ensemble is able to provide both interpretable channel relevance
and competitive classification accuracy, even when applied directly to the raw
brain data, without the need for feature engineering.
