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

Rafeed Alkawadri

doi:10.3389/fnins.2019.00191

Abstract

The main applications of the Brain–Computer Interface (BCI) have been in the domain of rehabilitation, control of prosthetics, and in neuro-feedback. Only a few clinical applications presently exist for the management of drug-resistant epilepsy. Epilepsy surgery can be a life-changing procedure in the subset of millions of patients who are medically intractable. Recording of seizures and localization of the Seizure Onset Zone (SOZ) in the subgroup of “surgical” patients, who require intracranial-EEG (icEEG) evaluations, remain to date the best available surrogate marker of the epileptogenic tissue. icEEG presents certain risks and challenges making it a frontier that will benefit from optimization. Despite the presentation of several novel biomarkers for the localization of epileptic brain regions (HFOs-spikes vs. Spikes for instance), integration of most in practices is not at the prime time as it requires a degree of knowledge about signal and computation. The clinical care remains inspired by the original practices of recording the seizures and expert visual analysis of rhythms at onset. It is becoming increasingly evident, however, that there is more to infer from the large amount of EEG data sampled at rates in the order of less than 1 ms and collected over several days of invasive EEG recordings than commonly done in practice. This opens the door for interesting areas at the intersection of neuroscience, computation, engineering and clinical care. Brain–Computer interface (BCI) has the potential of enabling the processing of a large amount of data in a short period of time and providing insights that are not possible otherwise by human expert readers. Our practices suggest that implementation of BCI and Real-Time processing of EEG data is possible and suitable for most standard clinical applications, in fact, often the performance is comparable to a highly qualified human readers with the advantage of producing the results in real-time reliably and tirelessly. This is of utmost importance in specific environments such as in the operating room (OR) among other applications. In this review, we will present the readers with potential targets for BCI in caring for patients with surgical epilepsy.

Highlights

Reviewed by: Adam Olding Hebb, Colorado Neurological Institute (CNI), United States Bachir Estephan, Mayo Clinic Arizona, United States Vassiliy Tsytsarev, University of Maryland, College Park, United States
It is an active area of research to optimize detections within functional brain regions that are most important for the surgical decision and to exclude less relevant ones
We have shown that a new metric we labeled the connectivity index (Alkawadri et al, 2013) which is based on the normalized number of averaged evoked responses to single pulse electrical stimulation weighted by the normalized distance at which the responses recorded at

Summary

Introduction

The issue of spatial sampling is a pertinent one in any research involving icEEG, as generally speaking there is a consistent bias toward sampling from epileptic brain regions, and this fact should be incorporated into the interpretation of available literature reporting on predictive values of markers of function or epilepsy. It is an active area of research to optimize detections within functional brain regions that are most important for the surgical decision and to exclude less relevant ones (i.e., increase the clinical specificity).

Results

Conclusion