A journal paper got accepted!

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Cho et al. 2023 Cerebral Cortex (in press)

Title: Unexpected sound omissions are signaled in human posterior superior temporal gyrus: an intracranial study  

Hohyun Cho1,2, Yvonne M. Fonken3,4, Markus Adamek1,2,5, Richard Jimenez3, Jack Lin6, Gerwin Schalk7, Robert T. Knight3,4, Peter Brunner1,2,8

  1. Department of Neurosurgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  2. National Center for Adaptive Neurotechnologies, Albany, New York, USA.
  3. Helen Wills Neuroscience Institute, University of California, Berkeley, USA
  4. Department of Psychology, University of California, Berkeley, USA
  5. Department of Neuroscience, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  6. School of Medicine, University of California, Davis, USA
  7. Frontier Lab for Applied Neurotechnology, Tianqiao and Chrissy Chen Institute, Shanghai, People’s Republic of China
  8. Department of Neurology, Albany Medical College, Albany, New York, USA.

Corresponding author:

Peter Brunner

Contact info: pbrunner@wustl.edu

Department of Neurosurgery, Washington University School of Medicine in St. Louis
660 S. Euclid Avenue, Campus Box 8057, St. Louis, MO 63110

Abstract

Context modulates sensory neural activations enhancing perceptual and behavioral performance and reducing prediction errors. However, the mechanism of when and where these high-level expectations act on sensory processing is unclear. Here, we isolate the effect of expectation absent of any auditory evoked activity by assessing the response to omitted expected sounds. Electrocorticographic signals were recorded directly from subdural electrode grids placed over the superior temporal gyrus (STG). Subjects listened to a predictable sequence of syllables, with some infrequently omitted. We found high-frequency band activity (HFA, 70-170 Hz) in response to omissions, which overlapped with a posterior subset of auditory-active electrodes in STG.  Heard syllables could be distinguishable reliably from STG, but not the identity of the omitted stimulus. Both omission- and target-detection responses were also observed in the prefrontal cortex.  We propose that the posterior STG is central for implementing predictions in the auditory environment. HFA omission responses in this region appear to index mismatch-signaling or salience detection processes.

Keywords: auditory cortex, ECoG, mismatch, prediction, salience

A patent has been registered!

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Cho, H., & Jun, S. C. (2019). Apparatus and method for brain computer interface. U.S. Patent No. US10413204B2. Washington, DC: U.S. Patent and Trademark Office.

2019-09-17 Publication of US10413204B2
2019-10-07 Application status is Active
2037-10-18 Adjusted expiration

Website: https://patents.google.com/patent/US10413204B2/en

Abstract
The present disclosure discloses an apparatus for a brain computer interface (BCI) including a feature extraction filter trainer for training a feature extraction filter which minimizes an influence of a background brain wave while maximizing a difference between intended brain waves; and a classifier trainer for training a classifier for classifying the intended brain waves by using a feature vector obtained by filtering the intended brain wave at the feature extraction filter. With the apparatus, only the background brain wave is additionally measured, such that previous intended brain wave data can be reused and the brain wave can be classified more quickly and accurately.

Poster Presentation at sfn 2018

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Title: The Physiological Origin of Cortical Evoked Potentials.

Authors: Hohyun Cho1, Peter Brunner1,2, Ladan Moheimanian1,3, Sung Chan Jun4, Gerwin Schalk1,2,3

Affiliations:

  1. Center for Adapt. Neurotechnologies, Wadsworth Ctr., New York State Dept. of Health, Albany, NY, USA
  2. of Neurology, Albany Medical College, Albany, NY, USA
  3. of Biomed. Sci., State Univ. of New York, Albany, NY, USA
  4. School of Elect. Eng. and Comp. Sci., Gwangju Inst. of Sci. and Tech., Gwangju, South Korea

Abstract:

Event-related potentials (ERPs) have been used for decades for neuroscientific research and for clinical diagnosis of neurological disorders. ERPs are usually categorized by the type of event that they arise from, e.g., auditory evoked potentials (AEPs) or motor evoked potentials (MEPs), and are further categorized by the latency and polarity of their constituent components (e.g., N1, P1, P2). Prior research has primarily considered three main mechanisms to give rise to ERPs: (1) additive contributions to ongoing activity; (2) phase resetting by a sensory stimulus; (3) oscillatory voltage asymmetry.

While different previous work has provided some evidence for each of these three possibilities, their specific quantitative contribution to each of an ERP’s individual components has not yet been determined. This lack of knowledge greatly impedes detailed physiological interpretation of ERPs and the generation of more general models that such data could inform. In our study, we began to address this important issue by quantifying the specific contribution of each of these mechanisms in the context of AEPs and MEPs.

In our study, eight human subjects who were implanted with electrocorticographic (ECoG) electrodes over STG and M1 motor cortex participated in a simple reaction time task. In this task, the subjects responded to a salient auditory stimulus by pressing a push button with the thumb contralateral to the ECoG implant. To determine the specific contribution of each of the three possible generating mechanisms to AEP and MEP responses, we assessed the fraction of the overall signal accounted for by each of them.

Our results demonstrate that MEPs are largely generated through additivity (88%) but not through phase reset (12%). The P1 and N1 components of the AEPs are mostly generated by phase reset (94%), while the P2 component is generated by additivity (57%) and phase reset (36%). Oscillatory voltage asymmetry only marginally contributed to these ERP components.  These results sheds light on the previously unknown contribution that additive, phase reset, and asymmetric amplitude mechanisms have on the generation of AEPs and MEPs. They should greatly facilitate the physiological interpretation of AEPs and MEPs, and should also be important for the creation of general models of evoked responses and their relationship to behavior.

Keywords: Event-Related Potentials (ERPs), Electrocorticography (ECoG)

Support:

  • NIH/NIBIB (P41-EB018783)
  • NIH/NICHD (R25-HD088157)
  • NIH/NIMH (P50-MH109429)
  • US Army Research Office (W911NF-14-1-0440)
  • Fondazione Neurone
  • National Research Foundation of Korea (No. 2016R1A2B4010897)

Categories:

D.06. Auditory & Vestibular Systems

  • 06.g. Auditory processing: Neural coding, experiment, and theory
  • 06.h. Auditory processing: Perception, cognition, and action

E.05. Brain-Machine Interface

  • 05.c. Neurophysiology: Neural processing

H.02. Human Cognition and Behavior

  • 02.t. Timing and temporal processing

Patent

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Cho, H., & Jun, S. C. (2017). U.S. Patent No. 20,170,238,831. Washington, DC: U.S. Patent and Trademark Office.

Website: http://www.freepatentsonline.com/y2017/0238831.html

Abstract
The present disclosure discloses an apparatus for a brain computer interface (BCI) including a feature extraction filter trainer for training a feature extraction filter which minimizes an influence of a background brain wave while maximizing a difference between intended brain waves; and a classifier trainer for training a classifier for classifying the intended brain waves by using a feature vector obtained by filtering the intended brain wave at the feature extraction filter. With the apparatus, only the background brain wave is additionally measured, such that previous intended brain wave data can be reused and the brain wave can be classified more quickly and accurately.

Journal publication

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Cho, H., Ahn, M., Ahn, S., Kwon, M., & Jun, S. C. (2017). EEG datasets for motor imagery brain computer interface. GigaScience. doi: 10.1093/gigascience/gix034

Abstract
Background: Most investigators of brain–computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)–based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. Findings: We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Conclusions: Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states.