首页|Research on Machine Learning Published by Researchers at Drexel University (Brai n-age estimation with a low-cost EEG-headset: effectiveness and implications for large-scale screening and brain optimization)

Research on Machine Learning Published by Researchers at Drexel University (Brai n-age estimation with a low-cost EEG-headset: effectiveness and implications for large-scale screening and brain optimization)

扫码查看
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting out of Philadelphia, Pennsylv ania, by NewsRx editors, research stated, “Over time, pathological, genetic, env ironmental, and lifestyle factors can age the brain and diminish its functional capabilities.” Our news correspondents obtained a quote from the research from Drexel Universit y: “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 deve lop a method for assessing general age-related brain health and function that ca n be implemented widely and inexpensively. To this end, we trained a machine-lea rning algorithm on restingstate EEG (RS-EEG) recordings obtained from healthy i ndividuals as the core of a brain-age estimation technique that takes an individ ual’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 machinelearning model against an independent test-set of healthy participants and obtained a correlation coefficient of 0.582 between the chronological and es timated brain ages (r = 0.963 after statistical bias-correction). The test-retes t correlation was 0.750 (0.939 after bias-correction) over a period of 1 week.”

Drexel UniversityPhiladelphiaPennsyl vaniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(MAY.7)