Next Article in Journal
Mapping Distributional Semantics to Property Norms with Deep Neural Networks
Previous Article in Journal
A Data-Driven Machine Learning Approach for Corrosion Risk Assessment—A Comparative Study
Open AccessArticle

Assessment of Cognitive Aging Using an SSVEP-Based Brain–Computer Interface System

1
Southwestern Educational Society High School, Camino Pitillo, Sector Cuba, Mayagüez, PR 00682, USA
2
Dept. of Electrical and Computer Engineering, University of Puerto Rico at Mayaguez, PR-108, Mayagüez, PR 00682, USA
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2019, 3(2), 29; https://doi.org/10.3390/bdcc3020029
Received: 23 March 2019 / Revised: 17 May 2019 / Accepted: 20 May 2019 / Published: 24 May 2019
Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and its timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain–computer interfaces (BCIs) stimulate and analyze key cerebral rhythms, enabling reliable cognitive assessment that can accelerate diagnosis. The BCI system presented analyzes steady-state visually evoked potentials (SSVEPs) elicited in subjects of varying age to detect cognitive aging, predict its magnitude, and identify its relationship with SSVEP features (band power and frequency detection accuracy), which were hypothesized to indicate cognitive decline due to aging. The BCI system was tested with subjects of varying age to assess its ability to detect aging-induced cognitive deterioration. Rectangular stimuli flickering at theta, alpha, and beta frequencies were presented to subjects, and frontal and occipital Electroencephalographic (EEG) responses were recorded. These were processed to calculate detection accuracy for each subject and calculate SSVEP band power. A neural network was trained using the features to predict cognitive age. The results showed potential cognitive deterioration through age-related variations in SSVEP features. Frequency detection accuracy declined after age group 20–40, and band power declined throughout all age groups. SSVEPs generated at theta and alpha frequencies, especially 7.5 Hz, were the best indicators of cognitive deterioration. Here, frequency detection accuracy consistently declined after age group 20–40 from an average of 96.64% to 69.23%. The presented system can be used as an effective diagnosis tool for age-related cognitive decline. View Full-Text
Keywords: brain–computer interface; cognitive aging; steady-state visually evoked potential; neural network; detection accuracy; band power brain–computer interface; cognitive aging; steady-state visually evoked potential; neural network; detection accuracy; band power
Show Figures

Figure 1

MDPI and ACS Style

Sridhar, S.; Manian, V. Assessment of Cognitive Aging Using an SSVEP-Based Brain–Computer Interface System. Big Data Cogn. Comput. 2019, 3, 29.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop