  In this work, we delve into the EEG classification task in the domain of
visual brain decoding via two frameworks, involving two different learning
paradigms. Considering the spatio-temporal nature of EEG data, one of our
frameworks is based on a CNN-BiLSTM model. The other involves a CNN-Transformer
architecture which inherently involves the more versatile attention based
learning paradigm. In both cases, a special 1D-CNN feature extraction module is
used to generate the initial embeddings with 1D convolutions in the time and
the EEG channel domains. Considering the EEG signals are noisy, non stationary
and the discriminative features are even less clear (than in semantically
structured data such as text or image), we also follow a window-based
classification followed by majority voting during inference, to yield labels at
a signal level. To illustrate how brain patterns correlate with different image
classes, we visualize t-SNE plots of the BiLSTM embeddings alongside brain
activation maps for the top 10 classes. These visualizations provide insightful
revelations into the distinct neural signatures associated with each visual
category, showcasing the BiLSTM's capability to capture and represent the
discriminative brain activity linked to visual stimuli. We demonstrate the
performance of our approach on the updated EEG-Imagenet dataset with positive
comparisons with state-of-the-art methods.
