10.5061/DRYAD.4F92N
Pahwa, Mrinal
Washington University in St. Louis
Kusner, Matthew
Washington University in St. Louis
Hacker, Carl D.
Washington University in St. Louis
Bundy, David T.
Washington University in St. Louis
Weinberger, Kilian Q.
Washington University in St. Louis
Leuthardt, Eric C.
Washington University in St. Louis
Data from: Optimizing the detection of wakeful and sleep-like states for
future electrocorticographic brain computer interface applications
Dryad
dataset
2016
Brain Computer Interfaces
Human Cortex
Electrocorticography
2016-01-26T07:13:54Z
2016-01-26T07:13:54Z
en
https://doi.org/10.1371/journal.pone.0142947
4396705886 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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 ECoG-based 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.
SubA_Sleep1SubA_Sleep2_part1SubA_Sleep2_part2SubA_Wake1_part1SubA_Wake1_part2SubA_Wake2_part1SubA_Wake2_part2SubB_Sleep1SubB_Sleep2_part1SubB_Sleep2_part2SubB_Wake1SubB_Wake2_part1SubB_Wake2_part2SubC_Sleep1SubC_Sleep2_part1SubC_Sleep2_part2SubC_Wake_1SubC_Wake2SubD_Sleep1_part1SubD_Sleep1_part2SubD_Sleep2SubD_Wake1SubD_Wake2