Carbogen inhalation during non-convulsive status epilepticus: A quantitative exploratory analysis of EEG recordings.

OBJECTIVE: To quantify the effect of inhaled 5% carbon-dioxide/95% oxygen on EEG recordings from patients in non-convulsive status epilepticus (NCSE). METHODS: Five children of mixed aetiology in NCSE were given high flow of inhaled carbogen (5% carbon dioxide/95% oxygen) using a face mask for maximum 120s. EEG was recorded concurrently in all patients. The effects of inhaled carbogen on patient EEG recordings were investigated using band-power, functional connectivity and graph theory measures. Carbogen effect was quantified by measuring effect size (Cohen's d) between "before", "during" and "after" carbogen delivery states. RESULTS: Carbogen's apparent effect on EEG band-power and network metrics across all patients for "before-during" and "before-after" inhalation comparisons was inconsistent across the five patients. CONCLUSION: The changes in different measures suggest a potentially non-homogeneous effect of carbogen on the patients' EEG. Different aetiology and duration of the inhalation may underlie these non-homogeneous effects. Tuning the carbogen parameters (such as ratio between CO2 and O2, duration of inhalation) on a personalised basis may improve seizure suppression in future.

The detection and classification of the mental state elicited by humor from EEG patterns.

In this paper we investigate the use of EEG to detect the affective state of humor. The EEG of five subjects was recorded while they recalled humorous videos. Extracted frequency features were compared to a control state in which users where asked to remain in a neutral mental state. An ANOVA test performed on the two groups: neutral and humor recall found a statistically significant difference in the frequency range 28-32 Hz for a number of channels including T7 and P7. Both of which presented the greatest statistically significant results with p values of 0.009 and 0.0 respectively Furthermore, we demonstrate that these mental states can be classified using Principal Component Analysis followed by a 3 features Linear Discriminant Analysis resulting in a leave one out classification accuracy of 95%.