The aim of the study is to improve the usefulness of resting-state EEG in the diagnosis and management of patients with Transient Epileptic Amnesia (TEA). Transient Epileptic Amnesia is characterised by recurrent episodes of short-lived amnesia, typically lasting less than 1hour. The amnestic attacks may occur alongside semiology seen in temporal lobe epilepsy and the associated amnesia may be anterograde and retrograde in nature, often in conjunction with accelerated interictal long-term forgetting and atypical patchy autobiographical memory loss. Whilst episodes of amnesia in TEA are known to be associated with epilepsy, either as ictal or post-ictal manifestations, the underlying pathophysiology and network mechanisms are not well understood. Whilst EEG is commonly used in diagnosis and management of epilepsy, in some cases the EEG is either normal or abnormalities are non-specific. Novel methods of biomedical processing now allow the exploration of networking and connectivity within the brain, using routine EEG data. These methods have been successfully used to gain further insight into conditions such as Alzheimer’s disease and other forms of cognitive impairment e.g. related to epilepsy. The main study objective is to evaluate networking and connectivity measures in patients with TEA, using resting-state EEG, and to compare these to a healthy age-matched population. The expectation is to identify networking and connectivity changes which are unique to the condition.