Brain-age estimation with a low-cost EEG-headset: effectiveness and implications for large-scale screening and brain optimization - Kounios, John; Fleck, Jessica I.; Zhang, Fengqing; Oh, Yongtaek (Frontiers in Neuroergonomics, 2024)
Over time, pathological, genetic, environmental, and lifestyle factors can age the brain and diminish its functional capabilities. While these factors can lead to disorders that can be diagnosed and treated once they become symptomatic, often treatment is difficult or ineffective by the time significant overt symptoms appear. One approach to this problem is to develop a method for assessing general age-related brain health and function that can be implemented widely and inexpensively. To this end, we trained a machine-learning algorithm on resting-state EEG (RS-EEG) recordings obtained from healthy individuals as the core of a brain-age estimation technique that takes an individual's RS-EEG recorded with the low-cost, user-friendly EMOTIV EPOC X headset and returns that person's estimated brain age. We tested the current version of our machine-learning model against an independent test-set of healthy participants and obtained a correlation coefficient of .582 between the chronological and estimated brain ages (r = .963 after statistical bias-correction). The test-retest correlation was .750 (.939 after bias-correction) over a period of one week. Given these strong results and the ease and low cost of implementation, this technique has the potential for widespread adoption in the clinic, workplace, and home as a method for assessing general brain health and function and for testing the impact of interventions over time.