IEEE Transactions on Biomedical Engineering

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Align and Pool for EEG Headset Domain Adaptation (ALPHA) to Facilitate Dry Electrode Based SSVEP-BCI
This study leverages transfer learning to improve the performance for steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) implemented by dry electrodes. We utilize auxiliary individual electroencephalogram (EEG) recorded from wet electrode for cross-device transfer learning via the proposed framework named ALign and Pool for EEG Headset domain Adaptation (ALPHA), which aligns the SSVEP features by domain adaptation. ALPHA significantly outperformed the competing methods in two transfer directions, and boosted the dry-electrode systems using wet-electrode EEG. The cross-device transfer learning by ALPHA could increase the utility and potentially promote the use of dry electrode based SSVEP-BCIs in practical applications... Read more
Featured Articles
Inter-and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-based BCIs
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that can deliver high information transfer rate (ITR) usually require subject’s calibration data to learn the class-and subject-specific model parameters (e.g. the spatial filters and SSVEP templates). Normally, the amount of the calibration data for learning is proportional to the number of classes (or visual stimuli), which could be huge and consequently lead to a time-consuming calibration... Read more