Autobiographical events help us to analyze our own thoughts and behavior over a period of time. Analyzing the retrieval of memory helps in better understanding of the disorders. This chapter aims at analyzing 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.
TopIntroduction
Memory is our ability to encode, store, retain and subsequently recall information and past experiences in the human brain.
It can be thought of in general terms as the use of experience to affect or influence current behaviour. Memory is the sum total of what we remember, and gives us the capability to learn and adapt from previous experiences as well as to build relationships. It is the ability to remember past experiences, and the power or process of recalling previously learned facts, experiences, impressions, skills and habits (Aizenstein et al., 2004). It is the storage of things learned and retained from our activity or experience, as evidenced by modification of structure or behaviour, or by recall and recognition. Current theoretical accounts of human episodic memory emphasize the subjective “re-living” that accompanies the retrieval of events (Yonelinas, 2001).
These details cause a subjective sense of re-experiencing that is not present for factual or semantic knowledge, which can be accomplished in the absence of autobiographical recollection (Friston et al., 2003). On the other hand, works were also done on speech imagery, where the brain connectivity parameters were found out and analysed (Kavitha & Sandhya, 2015). In addition to them blind source separation techniques such as independent components analysis and partial least squares were also implemented (Krakow & Fish, 1998). These methods support the estimation of signal space topography, but not provide a measure of connectivity between specific pairs of nodes, and therefore lie outside the scope of this paper (Cabeza, Kapur, & Craik, 2000)/
Generally, psychological studies utilize EEG to study the brain processes underlying attention, learning and memory. Hence this is an approach to bridge the data acquired to map and analyse the connectivity between various regions involved during the retrieval process, (Bamford, Murray, & Willshaw, 2010) but mapping of regions for various emotion-based events has not been significantly established. From task-based EEG we can estimate connectivity parameters through which we can perform forward and inverse modelling techniques to understand the active anatomical template and electrical model, of the subregions which were active during the task (Keyvan Mahjoory,2017). Machine learning algorithms had therefore played a crucial role in several important EEG-based research and application areas by allowing us to extract the information from EEG recordings of brain activity, and still there is a lot of room for considerable improvement with respect to several important aspects of information extraction from the EEG, including its accuracy, interpretability, and usability for online applications (Robin Schirrmeister, 2017).
Episodic memory is related to but distinct from learning, which is the process by which we acquire knowledge of the world and modify our subsequent behaviour (Basar, Karakas, & Schürmann, 2001). The hurdle to analyse the involvement of regions of brain during retrieval of events can be overcome by implementing the application of cognitive informatics, for example, during learning, neurons that fire together to produce an experience is altered so that they tend to fire together again.