Ongoing EEG artifact correction using blind source separation.

OBJECTIVE: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. METHODS: The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. RESULTS: The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time. CONCLUSIONS: Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. SIGNIFICANCE: The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.

Fast evaluation of interictal spikes in long-term EEG by hyper-clustering.

PURPOSE: The burden of reviewing long-term scalp electroencephalography (EEG) is not much alleviated by automated spike detection if thousands of events need to be inspected and mentally classified by the reviewer. This study investigated a novel technique of clustering and 24-h hyper-clustering on top of automated detection to assess whether fast review of focal interictal spike types was feasible and comparable to the spikes types observed during routine EEG review in epilepsy monitoring. METHODS: Spike detection used a transformation of scalp EEG into 29 regional source activities and adaptive thresholds to increase sensitivity. Our rule-based algorithm estimated 18 parameters around each detected peak and combined multichannel detections into one event. Similarity measures were derived from equivalent location, scalp topography, and source waveform of each event to form clusters over 2-h epochs using a density-based algorithm. Similar measures were applied to all 2-h clusters to form 24-h hyper-clusters. Independent raters evaluated electroencephalography data of 50 patients with epilepsy (25 children) using traditional visual spike review and optimized hyper-cluster inspection. Congruence between visual spike types and epileptiform hyper-clusters was assessed on a sublobar level using three-dimensional (3D) peak topographies. KEY FINDINGS: Visual rating found 126 different epileptiform spike types (2.5 per patient). Independently, 129 hyper-clusters were classified as epileptiform and originating in separate sublobar regions (2.6 per patient). Ninety-one percent of visual spike types matched with hyper-clusters (temporal lobe spikes 94%, extratemporal 89%). Conversely, 11% of hyper-clusters rated epileptiform had no corresponding visual spike type. Numbers were comparable in adults and children. On average, 15 hyper-clusters had to be inspected and rated per patient with an evaluation time of around 5 min. SIGNIFICANCE: Hyper-clustering over 24 h provides an independent tool for rapid daily evaluation of interictal spikes in long-term video-EEG monitoring. If used in addition to routine review of 2-5 min EEG per hour, sensitivity and reliability in noninvasive diagnosis of focal epilepsy increases.