Working Paper Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 10 May 2019 / Approved: 13 May 2019 / Online: 13 May 2019 (08:32:23 CEST)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: Big Data Cogn. Comput. 2019, 3, 29
DOI: 10.3390/bdcc3020029


Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and their 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 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, 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.


brain-computer Interface; cognitive aging; steady-state visual evoked potential, neural network; detection accuracy; band power


ENGINEERING, Biomedical & Chemical Engineering

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