Epilepsy
Conditions
Interventions
Group 1: The target variable is brain activity during conscious and unconscious processing of visual stimuli of varying durations. The stimuli presented are taken from a previous study. The critical s
patients are instructed to respond by pressing a button.
For each participant, the experiment consists of two trials, which are completed one after the other, ideally on different days. The first tria
Sponsors
Universitätsmedizin Frankfurt am Main
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Inclusion criteria
Inclusion criteria: Patients who, as part of preoperative diagnostics, had stereo-EEG electrodes implanted in the medial temporal lobe according to the bitemporal standard, orthogonal to the skull surface. This patient population consists of patients with drug-resistant temporal lobe epilepsy.
Exclusion criteria
Exclusion criteria: - Alcoholism, medication and drug abuse - Persons lacking legal capacity.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The primary endpoint is the difference in amplitude- and time-dependent event-related potentials (ERPs) between conditions of conscious and unconscious perception of varying durations, identified using cluster statistics across time and electrodes. Specifically, for each stimulus duration, ERPs are examined in the time window of 200 ms before and 3000 ms after the onset of stimulus presentation. For the ERP analyses, we perform a threshold-free cluster enhancement (TFCE) analysis separately for each duration, i.e., we compare the contrasts of Face-Scramble 1 between the conscious and unconscious groups. | — |
Secondary
| Measure | Time frame |
|---|---|
| The secondary endpoint is the time-dependent classification accuracy for predicting the duration of conscious perception based on multivariate EEG patterns (multivariate pattern analysis, MVPA). The algorithm we have chosen is linear discriminant analysis (LDA), a standard classifier that is easy to interpret. MVPA is a method in which, in the first step, a portion of the available data is used to fit a model (training), and in the second step, previously unknown data is used for classification based on this model and for evaluating model performance (validation). | — |
Countries
Germany
Contacts
Public ContactAntje Peters
Universitätsmedizin Frankfurt am Main
Outcome results
None listed