4 accepted papers
We introduce an innovative approach to automated sleep stage classification using electrooculogram (EOG) signals, addressing the discomfort and impracticality associated with electroencephalogram (EEG) data acquisition. In addition, this approach is untapped in the field, highlighting its potential…
A vast literature on brain encoding has effectively harnessed deep neural network models for accurately predicting brain activations from visual or text stimuli. Unfortunately, there is not much work on brain encoding for speech stimuli. The few existing studies on brain encoding for speech stimuli…
Can we build multi-view decoders that can decode concepts from brain recordings corresponding to any view (picture, sentence, word cloud) of stimuli? Can we build a system that can use brain recordings to automatically describe what a subject is watching using keywords or sentences? How about a syst…
Brain encoding aims at reconstructing fMRI brain activity given a stimulus. There exists a plethora of neural encoding models which study brain encoding for single mode stimuli: visual (pretrained CNNs) or text (pretrained language models). Few recent papers have also obtained separate visual and te…