Decoding Visual Imagination and Perception from EEG via Topomap Sequences

We propose a Topomap-based EEG decoding framework for distinguishing pictorial Imagination from Perception. By converting each trial's EEG signals into dense sequences of scalp voltage maps at short time intervals, our approach captures crucial spatiotemporal patterns that standard methods may overlook. We then apply a CNN with squeeze-and-excitation (SE) blocks to these Topomap "frames," enabling direct learning of both spatial topographies and rapid temporal fluctuations. Despite using only one trial per subject to simulate a data-scarce scenario, our model achieves 95.1% accuracy under a leave-one-subject-out (LOSO) cross-validation scheme. Results indicate clear neural distinctions between Imagination and Perception states, reflecting focused brain-region engagement during visual recall. In addition to confirming the viability of Topomaps as EEG feature representations, this study underscores their potential generalizability. We anticipate future extensions incorporating other modalities (orthographic, audio) and more advanced deep architectures will further expand the utility and robustness of this approach for brain-computer interface (BCI) applications.Clinical relevance- This framework offers a robust method for accurately distinguishing visual Imagination from Perception, even in data-scarce scenarios. It holds potential for enhancing diagnostic tools in cognitive disorders and refining BCI applications in clinical settings.