The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs to monitor mental workload (MWL) states in real-world settings. This study investigated the day-to-day stability of the raw power spectral density (PSD) and their periodic and aperiodic components decomposed by the Fitting Oscillations and One-Over-F algorithm. In addition, we validated the feasibility of using periodic components to improve cross-day MWL classification performance. Compared to the raw PSD (69.9%±18.5%) and the aperiodic component (69.4%±19.2%), the periodic component had better day-to-day stability and significantly higher cross-day classification accuracy (84.2%±11.0%). This finding not only enhances the practicality of pBCIs for MWL estimation but also unlocks the potential for decoding various brain states in future applications.
Keyphrases
- machine learning
- resting state
- deep learning
- functional connectivity
- working memory
- big data
- artificial intelligence
- endothelial cells
- mental health
- white matter
- cerebral ischemia
- electronic health record
- human health
- multiple sclerosis
- risk assessment
- high resolution
- optical coherence tomography
- atomic force microscopy
- magnetic resonance
- mass spectrometry
- climate change
- induced pluripotent stem cells
- dual energy