Multivariate Connectivity Analysis
Research Papers
Introduction to Advances in EEG Connectivity
This special issue of the Journal of Neurotherapy has been devoted to Advances in EEG Connectivities. These purposes include providing education to our readers and collaboration among the scientists and authors. Multiple connectivity metrics have been defined with an emphasis on coherence and multivariate connectivity measures. The goals of connectivity measurements should include accuracy compared to known neurological networks and utility in assessment and application for intervention (e.g., EEG coherence training). It is hoped that the information contained in this special issue will form the basis for future advancements in EEG connectivity assessment and intervention.
View Full Paper →EEG-NeuroBioFeedback Treatment of Patients with Brain Injury: Part 1: Typological Classification of Clinical Syndromes
Background. A group of 27 patients with brain injury were treated by electroencephalographic (EEG) NeuroBioFeedback under drug-free conditions. They were studied for distribution in classes of major syndromes for evaluation of treatment efficiency and rehabilitation rates with respect to associated EEG and other physiological changes. Methods. A total of 48 clinical symptoms were listed, each present in at least one patient. Classes of clinical signs have been computed using both medical and statistical criteria. Claimed and presented chief complaints, secondary complaints and all associated signs were incorporated in multivariate analysis. Results. Substantial intersection of medical and statistical distributions was observed. This provided a classification of symptoms into six classes representing the following syndromes of impaired functions: Q1 = motor; Q2 = language; Q3 = cognitive; Q4 = psychosocial; Q5 = pain-related; Q6(a & b) = neuropsychiatric; Q7 = metabolic. Membership of a patient in a defined clinical class was based on a numerical index computed from: (a) a weighted coefficient for the patient's chief and secondary complaints, and (b) an index for both symptoms represented in the class and symptoms not represented in the class. Patients were unambiguously distributed in all classes except Q7. Conclusions. Using anon-selected group of head injured patients, this work provides a rationale for the membership of each patient in a set of classes of syndromes determined by the whole set of clinical signs specifically exhibited by this group of patients. Class-average rehabilitation rates ranged from 59% up to 87% following an average 23 to 132 treatment sessions, depending on syndromes.
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