The effect of age on human motor electrocorticographic signals and implications for brain-computer interface applications

Jarod Roland, Kai Miller, Zac Freudenburg, Mohit Sharma, Matthew Smyth, Charles Gaona, Jonathan Breshears, Maurizio Corbetta, Eric C. Leuthardt

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Electrocorticography (ECoG)-based brain-computer interface (BCI) systems have emerged as a new signal platform for neuroprosthetic application. ECoG-based platforms have shown significant promise for clinical application due to the high level of information that can be derived from the ECoG signal, the signal's stability, and its intermediate nature of surgical invasiveness. However, before long-term BCI applications can be realized it will be important to also understand how the cortical physiology alters with age. Such understanding may provide an appreciation for how this may affect the control signals utilized by a chronic implant. In this study, we report on a large population of adult and pediatric invasively monitored subjects to determine the impact that age will have on surface cortical physiology. We evaluated six frequency bands - delta (<4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), low gamma band (30-50 Hz), and high gamma band (76-100 Hz) - to evaluate the effect of age on the magnitude of power change, cortical area of activation, and cortical networks. When significant trends are evaluated as a whole, it appears that the aging process appears to more substantively alter thalamocortical interactions leading to an increase in cortical inefficiency. Despite this, we find that higher gamma rhythms appear to be more anatomically constrained with age, while lower frequency rhythms appear to broaden in cortical involvement as time progresses. From an independent signal standpoint, this would favor high gamma rhythms' utilization as a separable signal that could be maintained chronically.

Original languageEnglish
Article number046013
JournalJournal of Neural Engineering
Issue number4
StatePublished - Aug 1 2011


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