Brain–Computer Interface (BCI) Applications in Mapping of Epileptic Brain Networks Based on Intracranial-EEG: An Update

The full article now available at

Brain–Computer Interface (BCI) Applications in Mapping of Epileptic Brain Networks Based on Intracranial-EEG: An Update

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Steve Jobs

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I wish to acknowledge research support by the American Epilepsy Society (award #412064) and CTSA Grant Number KL2TR000140 from the National Center for Advancing Translational Science (NCATS), a component of the National Institute of Health (NIH), the C.G. Swebilius Trust, and TUBITAK Grant Number 1059B191700801. Note, the contents of the manuscript is solely the responsibility of the author and do not necessarily represent the official view of the NIH.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The author wishes to thank Rebecca Khozein DOM, MS, REEG/EPT, RPSGT, RNCST, and Tamara Wing REEGT for assistance in EEG data acquisition.

Physiologic High Frequency Oscillations

Real-Time EEG amplifiers

Brain Computer Interface (BCI) Applications in Mapping of Epileptic Brain Networks based on Intracranial-EEG

By Rafeed Alkawadri

Update 3/27/2019: The full article in now available.

Read our recent article on breakthroughs in BCI applications in caring for patients with refractory epilepsy.

Read the full article at:

Brain Computer Interface (BCI) Applications in Mapping of Epileptic Brain Networks based on Intracranial-EEG

A quick look at a recent submission to the annual BCI society award and research trends available from the BCI community annual conference shows:

1. There is a steady increasing trend in BCI research with emphasis on epilepsy and movement disorders.

2. This constituted however, only 3.8% of the projects submitted. These numbers eclipsed by other uses.

There is a responsibility that falls on the shoulder of subspecialized funding agencies and supporting communities to augment research in this area which will continue to benefit patients with drug-resistant epilepsy, in the foreseen future, until, researchers identify less invasive, and more preemptive and efficient methods to treat epilepsy in the future.

In summary, icEEG data is the ideal medium for applications of artificial intelligence and machine learning in real-time. The applications within the domain of epilepsy surgery and seizure localization have lagged behind, however, the transformation is inevitable.