  Classifying EEG responses to naturalistic acoustic stimuli is of theoretical
and practical importance, but standard approaches are limited by processing
individual channels separately on very short sound segments (a few seconds or
less). Recent developments have shown classification for music stimuli (~2
mins) by extracting spectral components from EEG and using convolutional neural
networks (CNNs). This paper proposes an efficient method to map raw EEG signals
to individual songs listened for end-to-end classification. EEG channels are
treated as a dimension of a [Channel x Sample] image tile, and images are
classified using CNNs. Our experimental results (88.7%) compete with
state-of-the-art methods (85.0%), yet our classification task is more
challenging by processing longer stimuli that were similar to each other in
perceptual quality, and were unfamiliar to participants. We also adopt a
transfer learning scheme using a pre-trained ResNet-50, confirming the
effectiveness of transfer learning despite image domains unrelated from each
other.
