Meditation and cognitive load
EEG, HRV and deep-learning studies of mantra meditation, stress and neural efficiency.
Research area
Mechanistic research on attention, meditation, self-reflection, agency and affect using physiological signals and interpretable computational methods.
Scope
We combine EEG, heart-rate variability and behavioural measures to investigate attention, mind wandering, meditation, agency and complex emotion. Experimental questions are paired with computational methods that can distinguish meaningful structure from noise and individual variability.
The programme emphasises interpretable and reproducible biomarkers. Persistent homology, graph analysis, spectral measures and carefully evaluated learning systems are used to connect neural and autonomic dynamics with lived cognitive states without overstating what physiological data can establish.
Current directions
EEG, HRV and deep-learning studies of mantra meditation, stress and neural efficiency.
Neural and autonomic markers of attentive and distracted states, including differences associated with expertise.
Experimental analysis of how modes of reflection modulate experienced control and neural complexity.
Computational characterisation of complex affective states and multimodal signals.
Research record
Peer-reviewed work, chapters and current manuscripts connected with this research theme.
IEEE Sensors Journal 26(1), 1088–1102
https://doi.org/10.1109/JSEN.2025.3628683PLOS ONE 20(12), e0335276
https://doi.org/10.1371/journal.pone.0335276Biomedical Signal Processing and Control 89, 105779
https://doi.org/10.1016/j.bspc.2023.105779Manuscript
Manuscript under review
ChemRxiv · under review
https://doi.org/10.26434/chemrxiv-2025-fbmlfSpringer · Cognitive Biomarkers and BCI Applications
https://doi.org/10.1007/978-3-032-20634-3_10Springer · Cognitive Biomarkers and BCI Applications
https://doi.org/10.1007/978-3-032-20634-3_12Academic Press · Biological Measures of Well-Being
https://doi.org/10.1016/B978-0-443-28842-5.00001-6