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Voice pitch affects perceived authoritativeness, competency, and leadership capacity. In this study, the authors suggest that examining certain measures of brain activity collected using an affordable EEG could predict advertising effectiveness, which may be invaluable in future neuromarketing research. Understanding voice pitch and other factors that cause implicit bias may allow significant advances in marketing, facilitating business success.

Alzheimer’s disease (AD) is a common disease affecting 6 million people in the U.S., but no cure exists. To create therapy for AD, it is critical to detect amyloid-β protein in the brain at the early stage of AD because the accumulation of amyloid-β over 20 years is believed to cause memory impairment. However, it is difficult to examine amyloid-β in patients’ brains. In this study, we hypothesized that we could accurately predict the presence of amyloid-β using EEG data and machine learning.

In this article, Choi and Rossitto investigated the limitations of virtual learning by examining in-person dance learning compared to virtual dance learning while wearing EEG headsets. They found that in-person learners outperformed virtual learners and that virtual learners had higher mirror neuron activity as assessed by Mu rhythm power.

Cardiac autonomic and structural changes may occur in temporal lobe epilepsy patients and contribute to the risk of sudden unexpected death in epilepsy patients. Choi and colleagues reviewed clinical charts to obtain patients’ lifetime seizure count, antiepileptic drug use, and history of heart disease, followed by transthoracic echocardiogram to calculate left ventricle dimensions, ejection fraction, and left ventricle mass. By comparing epilepsy patients to control subjects, they found that epilepsy patients had thinner left ventricle walls and smaller ejection fraction, but with no significant difference in left ventricle mass.