Topic: EEG coherence

Identification of mental states in patients based on electrical activity is of interest for clinical neuroscience. While the problem has been successfully addressed for individual patients with multiple (up to 50) states identifiable for the purposes of communication, addressing the problem in a general sense is more difficult, as it requires understanding of the neural bases for successful classification. We used a range of well-established machine learning methods on a two-state classification problem based on EEG data with high success rate (>98%), and then applied Sparse Optimal Scoring

Selective attention is a crucial cognitive function facilitates the relevant-sensory-input processing (i.e., targets) and filters out irrelevant information (i.e., distractors). Prior studies illustrated the ability to selectively process behaviorally sensory information declines with age, especially in older adults who exhibit Mild Cognitive Impairment (MCI), a transitional stage from healthy aging to dementia, e.g. Alzheimer’s disease. Less is known regarding how neural mechanisms help eliminating distractors go awry in MCI. We hypothesized the fronto-parietal attentional network, which is i