  EEG technology finds applications in several domains. Currently, most EEG
systems require subjects to wear several electrodes on the scalp to be
effective. However, several channels might include noisy information, redundant
signals, induce longer preparation times and increase computational times of
any automated system for EEG decoding. One way to reduce the signal-to-noise
ratio and improve classification accuracy is to combine channel selection with
feature extraction, but EEG signals are known to present high inter-subject
variability. In this work we introduce a novel algorithm for
subject-independent channel selection of EEG recordings. Considering
multi-channel trial recordings as statistical units and the EEG decoding task
as the class of reference, the algorithm (i) exploits channel-specific
1D-Convolutional Neural Networks (1D-CNNs) as feature extractors in a
supervised fashion to maximize class separability; (ii) it reduces a high
dimensional multi-channel trial representation into a unique trial vector by
concatenating the channels' embeddings and (iii) recovers the complex
inter-channel relationships during channel selection, by exploiting an ensemble
of AutoEncoders (AE) to identify from these vectors the most relevant channels
to perform classification. After training, the algorithm can be exploited by
transferring only the parametrized subgroup of selected channel-specific
1D-CNNs to new signals from new subjects and obtain low-dimensional and highly
informative trial vectors to be fed to any classifier.
