Abstract :
This project aimes at bridging the gap between gold-standard clinical sleep monitoring and emerging wearable EEG technologies. The dataset comprises 128 nights in which healthy participants were simultaneously monitored with two technologies: a Brain Quick Plus Evolution PSG system, by Micromed, and a wearable EEG headband, by Bitbrain. The Micromed PSG system provides a comprehensive and clinically validated set of sleep parameters, while the Bitbrain wearable EEG headband offers a user-friendly, self-administered alternative, limited to forehead EEG electrodes.
A relevant aspect of the dataset is the simultaneous acquisition of data from both systems, allowing for direct comparison and validation of the wearable EEG device against the established PSG standard. This dual-recording approach provides a rich resource for evaluating the performance and potential of wearable EEG technology in sleep studies.
To ensure robust and reliable sleep staging, we employed a rigorous labeling process. Three expert sleep scorers independently annotated the PSG recordings following the criteria developed by the American Academy of Sleep Medicine (AASM) (Berry et al., 2015), and a consensus label was derived from these annotations by a fourth expert. This consensus labeling approach addresses the inherent variability in human sleep staging, which has an estimated inter-scorer agreement of approximately 85% (Danker-Hopfe et al., 2009; Rosenberg and Van Hout, 2013). The consensus labels were then applied to the corresponding wearable EEG recordings, leveraging the simultaneous data acquisition. Moreover, we utilized a deep learning model to analyze the dataset (Esparza-Iaizzo et al., 2024). By implementing a cross-validation procedure, we trained and validated the model separately on the PSG and wearable EEG datasets. The model achieved an 87.08% match between the human-consensus labels and the network-provided labels for the PSG data, and an 86.64% match for the wearable EEG data.
Eduardo López-Larraz and María Sierra-Torralba and Sergio Clemente and Galit Fierro and David Oriol and Javier Minguez and Luis Montesano and Jens G. Klinzing (2024). Bitbrain Open Access Sleep Dataset. OpenNeuro. [Dataset] doi: https://doi.org/10.18112/openneuro.ds005555.v1.0.0
