Aging Effects on the Performance of a Brain-computer Interface

  • Mei Lin Chen Department of Systems Design Engineering University of Waterloo
  • Dannie Fu Department of Systems Design Engineering University of Waterloo
  • Jennifer Boger Department of Systems Design Engineering University of Waterloo
  • Ning Jiang Department of Systems Design Engineering University of Waterloo

Abstract

The increasingly aging demographic poses a significant challenge to the over-burdened healthcare systems, and the society in general. In recent years, there is substantial research efforts in emerging technologies address various challenges related to aging. Brain-computer interface (BCI) is one of these technologies that could play an important role in aging-related applications such as post-stroke neurorehabilitation. Currently, various signal processing algorithms for electroencephalogram (EEG) used in BCIs have been developed using data from much younger populations than the average age of stroke survivors. It is unclear how age-related changes may affect the EEG signal and consequently the applicability of these algorithms in Stroke population. This research investigated the EEG response to haptic stimulation from 11 younger (21.7±2.76 year old) and 11 older (72.0±8.07 year old) subjects. The results showed that the average BCI performance accuracies of a classic two-class scenario between the two groups of subjects were significantly different. The performance of older subjects was 64.5±7.75%, more than 20% lower than that of the younger subjects (85.3±14.1%) and statistically significantly different ( t(20)= -4.3, p <0.001). Future research in BCIs for aging-related applications should further investigate the reasons for such difference and find strategies to address this performance gap between the two groups.
Published
2018-05-08
How to Cite
[1]
M. Chen, D. Fu, J. Boger, and N. Jiang, “Aging Effects on the Performance of a Brain-computer Interface”, CMBES, vol. 41, May 2018.
Section
Academic