No abstract

No abstract

BackgroundLarge amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets.MethodsThe auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks.ResultsThe algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate.ConclusionThe developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.

The ability of different short-term heart rate variability metrics to classify the level of mental workload (MWL) in 140 s segments was studied. Electrocardiographic data and event related potentials (ERPs), calculated from electroencephalographic data, were collected from 13 healthy subjects during the performance of a computerised cognitive multitask test with different task load levels. The amplitude of the P300 component of the ERPs was used as an objective measure of MWL. Receiver operating characteristics analysis (ROC) showed that the time domain metric of average interbeat interval length was the best-performing metric in terms of classification ability.

Individuals with job burnout symptoms often report having cognitive difficulties, but related electrophysiological studies are scarce. We assessed the impact of burnout on performing a visual task with varying memory loads, and on involuntary attention switch to distractor sounds using scalp recordings of event-related potentials (ERPs). Task performance was comparable between burnout and control groups. The distractor sounds elicited a P3a response, which was reduced in the burnout group. This suggests burnout-related deficits in processing novel and potentially important events during task performance. In the burnout group, we also observed a decrease in working-memory related P3b responses over posterior scalp and increase over frontal areas. These results suggest that burnout is associated with deficits in cognitive control needed to monitor and update information in working memory. Successful task performance in burnout might require additional recruitment of anterior regions to compensate the decrement in posterior activity.