TY - JOUR
T1 - Optimizing the detection of wakeful and sleep-like states for future electrocorticographic brain computer interface applications
AU - Pahwa, Mrinal
AU - Kusner, Matthew
AU - Hacker, Carl D.
AU - Bundy, David T.
AU - Weinberger, Kilian Q.
AU - Leuthardt, Eric C.
N1 - Publisher Copyright:
© 2015 Pahwa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoGbased BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging for target users (e.g., individuals with tetraplegia) due to severe motor disability. In this study, we present an automated and practical strategy to switch a BCI system on or off based on the cognitive state of the user. Using a logistic regression, we built probabilistic models that utilized sub-dural ECoG signals from humans to estimate in pseudo real-time whether a person is awake or in a sleep-like state, and subsequently, whether to turn a BCI system on or off. Furthermore, we constrained these models to identify the optimal anatomical and spectral parameters for delineating states. Other methods exist to differentiate wake and sleep states using ECoG, but none account for practical requirements of BCI application, such as minimizing the size of an ECoG implant and predicting states in real time. Our results demonstrate that, across 4 individuals, wakeful and sleep-like states can be classified with over 80% accuracy (up to 92%) in pseudo real-time using high gamma (70-110 Hz) band limited power from only 5 electrodes (platinum discs with a diameter of 2.3 mm) located above the precentral and posterior superior temporal gyrus.
AB - Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoGbased BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging for target users (e.g., individuals with tetraplegia) due to severe motor disability. In this study, we present an automated and practical strategy to switch a BCI system on or off based on the cognitive state of the user. Using a logistic regression, we built probabilistic models that utilized sub-dural ECoG signals from humans to estimate in pseudo real-time whether a person is awake or in a sleep-like state, and subsequently, whether to turn a BCI system on or off. Furthermore, we constrained these models to identify the optimal anatomical and spectral parameters for delineating states. Other methods exist to differentiate wake and sleep states using ECoG, but none account for practical requirements of BCI application, such as minimizing the size of an ECoG implant and predicting states in real time. Our results demonstrate that, across 4 individuals, wakeful and sleep-like states can be classified with over 80% accuracy (up to 92%) in pseudo real-time using high gamma (70-110 Hz) band limited power from only 5 electrodes (platinum discs with a diameter of 2.3 mm) located above the precentral and posterior superior temporal gyrus.
UR - http://www.scopus.com/inward/record.url?scp=84955517696&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0142947
DO - 10.1371/journal.pone.0142947
M3 - Article
C2 - 26562013
AN - SCOPUS:84955517696
SN - 1932-6203
VL - 10
JO - PloS one
JF - PloS one
IS - 11
M1 - e0142947
ER -