Functional Connectivity Assessment for Episodic Memory by Decoding Theta Wave

Mallampalli Kapardi and Kavitha Anandan
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Mallampalli Kapardi: Department of Biomedical Engineering, SSN College of Engineering, Chennai, India
Kavitha Anandan: Centre for Healthcare Technologies, Department of Biomedical Engineering, SSN College of Engineering, Chennai, India

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2018, vol. 12, issue 2, 17-31

Abstract: Autobiographical events help us to analyse our own thoughts and behaviour over a period of time. Analysing the retrieval of memory helps in better understanding of the disorders. This article aims at analysing the functional connectivity of young adults during a multiphase memory retrieval process. Subjects have been made to recall events in different phases of their life. EEG signals have been recorded while the subjects are performing their tasks. Inter-hemispherical coherence has been estimated from the processed EEG signals As theta band posed higher power compared to all other bands, it was considered for further analysis. A mathematical function was formed for the processed theta wave, to determine the coherence between various electrodes. The function generated a theta wave for every task and each wave was significant in its own way. The connectivity matrix was found to identify the active electrodes during retrieval of events. The results were validated by computing coherence separately for the same electrodes and for the same events.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:12:y:2018:i:2:p:17-31

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