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Full-Text Articles in Physical Sciences and Mathematics
Source-Free Domain Adaptation For Sleep Stage Classification, Yasmin Niknam
Source-Free Domain Adaptation For Sleep Stage Classification, Yasmin Niknam
Electronic Thesis and Dissertation Repository
The popularity of machine learning algorithms has increased in recent years as data volumes have risen, algorithms have advanced, and computational power and storage have improved. EEG-based sleep staging has become one of the most active research areas over the last decade. Labeling each sleep stage manually is a labor-intensive and time-consuming process that requires expertise, making it susceptible to human error. In the meantime, training models on an unseen dataset remains challenging due to physiological differences between subjects and electrode sensor configurations. Unsupervised domain adaptation approaches may provide a solution to this problem by borrowing knowledge from a labeled …
Attention-Based Multi-Source-Free Domain Adaptation For Eeg Emotion Recognition, Amir Hesam Salimnia
Attention-Based Multi-Source-Free Domain Adaptation For Eeg Emotion Recognition, Amir Hesam Salimnia
Electronic Thesis and Dissertation Repository
Electroencephalography (EEG) based emotion recognition in affective brain-computer interfaces has advanced significantly in recent years. Unsupervised domain adaptation (UDA) methods have been successfully used to mitigate the need for large amounts of training data, which is required due to the inter-subject variability of EEG signals. Typical UDA solutions require access to raw source data to leverage the knowledge learned from the labelled source domains (previous subjects) across the target domain (a new subject), raising privacy concerns. To tackle this issue, we propose Attention-based Multi-Source-Free Domain Adaptation (AMFDA) for EEG emotion recognition. AMFDA attempts to transfer knowledge of source models to …