Identifying true brain interaction from EEG data using the imaginary part of coherency

• Published in: Clinical neurophysiology Vol. 115; no. 10; pp. 2292 - 2307
• Main Authors: Nolte, Guido, Bai, Ou, Wheaton, Lewis, Mari, Zoltan, Vorbach, Sherry, Hallett, Mark
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier Ireland Ltd 01.10.2004; Elsevier Science
• Subjects: Algorithms; Alpha Rhythm; Beta Rhythm; Biological and medical sciences; Brain - physiology; Coherence; Connectivity; Data Interpretation, Statistical; EEG; Electrodiagnosis. Electric activity recording; Electroencephalography - statistics & numerical data; Functional Laterality - physiology; Fundamental and applied biological sciences. Psychology; Investigative techniques, diagnostic techniques (general aspects); Magnetoencephalography - statistics & numerical data; Medical sciences; Models, Neurological; Motor control; Motor control and motor pathways. Reflexes. Control centers of vegetative functions. Vestibular system and equilibration; Nervous system; Time-lag; Vertebrates: nervous system and sense organs; Volume conduction; Connectivity; Time-lag; Motor control; Volume conduction; EEG; Coherence; Magnetoencephalography; Interaction; Central nervous system; Electrophysiology; Electroencephalography; Encephalon; Electrodiagnosis; Body movement; Measurement method
• ISSN: 1388-2457; 1872-8952
• DOI: 10.1016/j.clinph.2004.04.029

Objective: The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. Methods: We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. Results: We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2–4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. Conclusions: It is possible to reliably detect brain interaction during movement from EEG data. Significance: The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.