Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions

Abstract

This paper introduces a freely accessible database http://eeg.pl/epi, containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time.

Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

“…Currently, the existing EEG databases available for research purposes mostly contain the EEG signals of motor imagination [38,39], sleep stages [40] and epilepsy [41], and only a few EEG databases related to the emotional states are available, such as Mahnob-HCI [42], the SJTU Emotion EEG Dataset (SEED) database [43] and the DEAP database [4]. This work utilized the DEAP database [4] that provided the most EEG data from participants; emotional stimuli came from video music clips, and the emotional states of the participants were defined in 2D emotion models (i.e., the arousal and the valence levels [4]), producing four quadrants of emotion: the high arousal, high valence (HAHV); the low arousal, high valence (LAHV); the low arousal, low valence (LALV); and the high arousal, low valence (HALV) emotion classes.…”

Intracranial Volume Estimation and Graph Theoretical Analysis of Brain Functional Connectivity Networks

“…The EEG data used for this chapter of the dissertation are part of a publicly accessible dataset (Zwoliński, Roszkowski et al 2010). The dataset includes continuous interictal EEG recordings of severe epilepsy patients who had undergone resection surgery.…”

“…The data were band-pass filtered for 0.5-70 Hz and notch filtered for 50 Hz. Table 4.1 provides the demographic information for the subject studies included in this chapter from the complete dataset (Zwoliński, Roszkowski et al 2010). …”

“…Epileptogenic waveforms marked by the epileptologist (Zwoliński, Roszkowski et al 2010) were used to segment EEG recordings to three different phases of the event by moving a window with the length of the event over the recordings in time:…”

“…Interictal EEG recordings take about 20-40 minutes, usually performed in an outpatient laboratory environment, to establish the presence of epilepsy as well as to serve for classifying the type of epilepsy (Focal or Generalized) (Pillai and Sperling 2006). Operation surgery to remove the epileptogenic brain tissue zone is currently the only possible cure for pharmaceutical-resistance patients; however epileptogenic foci localization is performed following invasive presurgical EEG recordings (Zwoliński, Roszkowski et al 2010). This chapter of the dissertation emphasizes utilization of our proposed FCN construction technique to study the FCNs related to pre-inter-and post-stages of the evolution of interictal EEG recordings.…”