title: Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Washington University, St. Louis, Missouri, United States of America.
authors.forename: Mrinal
authors.initials: M
authors.lastname: Pahwa
authors.affiliation: Department of Computer Science and Engineering, Washington University, St. Louis, Missouri, United States of America.
authors.forename: Matthew
authors.initials: M
authors.lastname: Kusner
authors.affiliation: Department of Biomedical Engineering, Washington University, St. Louis, Missouri, United States of America.|School of Medicine, Washington University, St. Louis, Missouri, United States of America.
authors.forename: Carl D
authors.initials: CD
authors.lastname: Hacker
authors.affiliation: Department of Biomedical Engineering, Washington University, St. Louis, Missouri, United States of America.
authors.forename: David T
authors.initials: DT
authors.lastname: Bundy
authors.affiliation: Department of Computer Science and Engineering, Washington University, St. Louis, Missouri, United States of America.
authors.forename: Kilian Q
authors.initials: KQ
authors.lastname: Weinberger
authors.affiliation: Department of Biomedical Engineering, Washington University, St. Louis, Missouri, United States of America.|School of Medicine, Washington University, St. Louis, Missouri, United States of America.|Department of Neurological Surgery, Washington University, St. Louis, Missouri, United States of America.|Center for Innovation in Neuroscience and Technology, Washington University, St. Louis, Missouri, United States of America.
authors.forename: Eric C
authors.initials: EC
authors.lastname: Leuthardt
mesh_terms: D000328:Adult; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D062207:Brain-Computer Interfaces; D000069280:Electrocorticography / Q000379:methods*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008953:Models, Anatomic; D012890:Sleep; D014851:Wakefulness; D055815:Young Adult