Search Results for author:
Found 5 papers, 0 papers with code
Improving self-supervised pretraining models for epileptic seizure detection from EEG data
Traditional supervised learning algorithms are often limited by the amount of labeled data, especially in the medical domain, where labeling is costly in terms of human processing and specialized experts needed to label them.
Predicting the imagined contents using brain activation
A support vector machine trained on the midbrain activation patterns to the visually presented rewards predicted with 75% accuracy whether the participants imagined the monetary reward or the scrambled picture during imagination trials.
BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings
We attain comparable performance utilizing less than 4\% of the parameters of other models.
Classifying Songs with EEG
This research study aims to use machine learning methods to characterize the EEG response to music.
Understanding Consumer Preferences for Movie Trailers from EEG using Machine Learning
Neuromarketing aims to understand consumer behavior using neuroscience.
