Data augmentation for learning predictive models on EEG: a systematic comparison
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/aca220
Abstract: Objective. The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation,… read more here.
Keywords: predictive models; augmentation learning; data augmentation; models eeg ... See more keywords
Foundation models for EEG decoding: current progress and prospective research
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/ae17e9
Abstract: Objective. Electroencephalography (EEG) records the spontaneous electrical activity in the brain. Despite the growing application of deep learning in EEG decoding, traditional methods still rely heavily on supervised learning, which is often limited by task… read more here.
Keywords: eeg decoding; eeg fms; research; models eeg ... See more keywords
A tutorial on fitting joint models of M/EEG and behavior to understand cognition
Sign Up to like & getrecommendations! Published in 2024 at "Behavior Research Methods"
DOI: 10.3758/s13428-023-02331-x
Abstract: We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and… read more here.
Keywords: cognition; joint models; fitting joint; eeg behavior ... See more keywords