Event-Related Functional Network Identification: Application to EEG Classification

• Published in: IEEE journal of selected topics in signal processing Vol. 10; no. 7; pp. 1284 - 1294
• Main Authors: Gonuguntla, Venkateswarlu, Wang, Yubo, Veluvolu, Kalyana C.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Band-pass filters; Classification; connectivity; Electrodes; electroencephalographic (EEG); Electroencephalography; Emulation; phase locking value (PLV); Phase measurement; Protocols; reactive band; Synchronization; threshold; Time-frequency analysis
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2016.2602007

Recent works on brain functional analysis have accentuated the importance of distributed functional networks and synchronized activity between networks in mediating cognitive functions. The network perspective is important to relate mechanisms of brain functions and the basis for classifying brain states. In this work, the network patterns related to neural tasks based on synchronization measure phase-locking value (PLV) in an electroencephalogram (EEG) are analyzed. Based on network dissimilarities between the rest and motor imagery tasks, important nodes and channel pairs corresponding to motor tasks are identified. A framework is developed to identify these most reactive channel pairs that form the subject-specific functional network. The identified functional network corresponding to tasks demonstrate significant PLV variation in line with the experiment protocol. With the selection of subject-specific reactive band, these channel pairs provide even higher variation corresponding to tasks. To demonstrate the potential of the developed framework to brain-computer interface, identified network patterns are employed as features for classification of tasks. Analysis performed with the two classes (left-hand and right-hand motor imagery EEG data) showed that the proposed approach yielded better classification results compared to earlier band-power based approaches for single trial analysis.