Spatial analysis of intracerebral electroencephalographic signals in the time and frequency domain: identification of epileptogenic networks in partial epilepsy

• Published in: Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences physical, and engineering sciences, 2009-01, Vol.367 (1887), p.297-316
• Main Authors: Wendling, Fabrice, Bartolomei, Fabrice, Senhadji, Lotfi
• Format: Article
• Language: English
• Subjects: Algorithms; Amygdala - physiopathology; Bioengineering; Brain; Brain - physiopathology; Cognitive science; Computer Science; Electrodes; Electroencephalography; Electroencephalography - statistics & numerical data; Electronic Data Processing; Engineering Sciences; Epilepsies, Partial - physiopathology; Epilepsy; Hippocampus; Hippocampus - physiopathology; Humans; Ictal; Interictal; Intracerebral; Life Sciences; Models, Anatomic; Nerve Net; Neuroscience; Partial epilepsies; Regression Analysis; Review; Scalp; Seizures; Signal and Image processing; Signal processing; Signal Processing, Computer-Assisted; Statistical Signal Processing; Temporal lobe
• ISSN: 1364-503X; 1471-2962
• DOI: 10.1098/rsta.2008.0220

Electroencephalography (EEG) occupies an important place for studying human brain activity in general, and epileptic processes in particular, with appropriate time resolution. Scalp EEG or intracerebral EEG signals recorded in patients with drug-resistant partial epilepsy convey important information about epileptogenic networks that must be localized and understood prior to subsequent therapeutic procedures. However, this information, often subtle, is 'hidden' in the signals. It is precisely the role of signal processing to extract this information and to put it into a 'coherent and interpretable picture' that can participate in the therapeutic strategy. Nowadays, the panel of available methods is very wide depending on the objectives such as, for instance, the detection of transient epileptiform events, the detection and/or prediction of seizures, the recognition and/or the classification of EEG patterns, the localization of epileptic neuronal sources, the characterization of neural synchrony, the determination of functional connectivity, among others. The intent of this paper is to focus on a specific category of methods providing relevant information about epileptogenic networks from the analysis of spatial properties of EEG signals in the time and frequency domain. These methods apply to either interictal or ictal recordings and share the common objective of localizing the subsets of brain structures involved in both types of paroxysmal activity. Most of these methods were developed by our group and are routinely used during pre-surgical evaluation. Examples are detailed. Results, as well as limitations of the methods, are also discussed.