Spatiotemporal EEG Dynamics of Prospective Memory in Ageing and Mild Cognitive Impairment

Crook-Rumsey, Mark; Howard, Christina J.; Doborjeh, Zohreh Gholami; Doborjeh, Maryam Gholami; Espinosa-Ramos, Josafath I.; Kasabov, Nikola; Sumich, Alexander

doi:10.1007/s12559-022-10075-7

Cited by 3 publications

(1 citation statement)

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“…To assess the brain function during these tasks, the spatial and temporal interaction needs to be studied. 56 Hence, we used PAC and PLV techniques in five different frequency bands through two types of couplings. 57,58 The first one being phase relationship in spatial domain and second one is phase and amplitude coupling in frequency domain.…”

Section: Discussionmentioning

confidence: 99%

Classification of cognitive impairment using electroencephalography for clinical inspection

Easwaran,

Ramakrishnan,

Jeyabal

2024

Proc Inst Mech Eng H

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Impairment in cognitive skill though set-in due to various diseases, its progress is based on neuronal degeneration. In general, cognitive impairment (CI) is divided into three stages: mild, moderate and severe. Quantification of CI is important for deciding/changing therapy. Attempted in this work is to quantify electroencephalograph (EEG) signal and group it into four classes (controls and three stages of CI). After acquiring resting state EEG signal from the participants, non-local and local synchrony measures are derived from phase amplitude coupling and phase locking value. This totals to 160 features per individual for each task. Two types of classification networks are constructed. The first one is an artificial neural network (ANN) that takes derived features and gives a maximum accuracy of 85.11%. The second network is convolutional neural network (CNN) for which topographical images constructed from EEG features becomes the input dataset. The network is trained with 60% of data and then tested with remaining 40% of data. This process is performed in 5-fold technique, which yields an average accuracy of 94.75% with only 30 numbers of inputs for every individual. The result of the study shows that CNN outperforms ANN with a relatively lesser number of inputs. From this it can be concluded that this method proposes a simple task for acquiring EEG (which can be done by CI subjects) and quantifies CI stages with no overlapping between control and test group, thus making it possible for identifying early symptoms of CI.

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Section: Discussionmentioning

confidence: 99%