Performance of an automated algorithm to process artefacts for quantitative EEG analysis during a simultaneous driving simulator performance task

- An automated algorithm to process artefacts from noisy wake EEG signals was tested.
- The overall accuracy of the algorithm was similar to that of the reference gold-standard.
- EEG spectral powers obtained using the two artefact-processing methods were similar.
- Increased slow frequency EEG power was significantly correlated with poorer driving.
- The algorithm can be an effective automated artefact-processing tool in EEG studies.

BackgroundArtefact removal from noisy EEG signals is cumbersome, and often requires manual intervention. We tested the performance of an automated method to detect and remove artefacts from EEG recorded during a driving simulation task.MethodsFive patients with obstructive sleep apnea (OSA) and five healthy controls were randomly selected from 17 participants undergoing a 40-h extended wakefulness study with 2-hourly 30-minute simulated driving tasks with simultaneous EEG. Two EEG recordings from each individual were studied. EEG data was first processed by independent component analysis (ICA). The accuracy of the automated algorithm (AA) to detect residual EEG artefact was evaluated against a reference-standard (RS) of visually identified artefact-contaminated epochs. EEG spectral power was calculated using 1) the RS method, 2) the AA, and 3) raw data without any artefact rejection (ICA only).ResultsThe algorithm showed good sensitivity (median: 83.9%), excellent specificity (91.1%), and high accuracy (87.0%) to detect noisy epochs. Cohen's κ indicated a substantial agreement between the two methods (0.72). EEG spectral power calculated using the RS and the AA did not differ significantly, while the power of the raw signal was significantly higher than those produced by any artefact rejection method. Increased EEG delta and theta power were significantly correlated with poorer driving performance.ConclusionsThese preliminary findings demonstrate an effective automated method to process EEG artefact recorded during driving simulation. This approach may facilitate the routine application of quantitative EEG analyses in future studies and identify new markers of impaired driving performance associated with sleep disorders.