Quantifying neural variability and learning during real world brain-computer interface use
量化现实世界脑机接口使用过程中的神经变异和学习
基本信息
- 批准号:10548865
- 负责人:
- 金额:$ 53.27万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-01-15 至 2026-11-30
- 项目状态:未结题
- 来源:
- 关键词:AccelerationAutomobile DrivingBiomimeticsCalibrationClinicalCognitiveComplementComputer softwareComputersCustomDataDevelopmentDevicesDimensionsDisabled PersonsElementsEnvironmentEnvironmental Risk FactorFatigueGoalsHomeHome environmentInterventionKnowledgeLaboratoriesLaboratory StudyLearningLightingLimb structureLongitudinal StudiesMeasuresMonitorMotionMotor CortexMovementMusNeuronal PlasticityNoiseOutputPainParticipantPatternPerformancePhysiologicalPopulationPositioning AttributePosturePropertyQuality of lifeSelf-Help DevicesSignal TransductionSourceSpeedStable PopulationsStressSystemTechnologyTestingTimeTrainingTranslatingTranslationsbrain computer interfaceclinical translationdesigndistractionenvironmental changeexperienceexperimental studygrasphome testimprovedmotor learningneuralneural patterningneuromechanismneurotransmissionpopulation basedportabilityresponseskillsvisual tracking
项目摘要
The performance of intracortical brain-computer interfaces (BCIs) has advanced substantially over the last
decade, but these devices are not yet robust enough for the home environment, where they can truly improve
quality of life for individuals with disabilities. To date, BCIs have depended on experienced technicians to operate
large and complicated systems comprised of multiple computers, signal processors, neural recording
headstages, and custom software. Our laboratory has developed a portable, battery-powered intracortical BCI
system that enables independent in-home computer access. However, to achieve the long-term goal of true
clinical viability, BCIs must also offer reliable and robust functional performance in the less well-controlled home
environment. We have achieved robust and generalizable control of a computer cursor using a biomimetic
approach that combines reach-based velocity control of cursor position with grasp-based control of mouse click
onset and offset. This transient-based neural decoder allows for generalized click function, adding the ability to
‘click-and-drag’ when accessing a computer (similar to carrying an object) to the ‘point-and-click’ functionality
that is typically implemented in BCIs. Independent home use of the BCI system will provide an opportunity to
collect neural data over long periods of time during unstructured and varied tasks, enabling quantification of
context-dependent neural variability due to subject-state (e.g., fatigue, pain, or stress) as well as plasticity due
to learning. Understanding how neural signals vary over time will be critical for clinical BCI systems that must be
robust, generalizable, and autonomous (i.e., operate for extended periods without technician intervention).
This project will first quantify the impact of subject-state on movement-related neural activity and performance
during in-home BCI use. This understanding is critical to developing robust BCIs that eliminate the need for
recalibration even in uncontrolled environments. The extent to which subject-state information is represented in
motor cortex and overlaps with BCI control dimensions will inform development efforts to mitigate the impact of
these nuisance variables. Participants will use the BCI for a variety of self-selected computer access tasks over
periods of many months that will challenge decoder performance. This project will investigate motor learning
mechanisms that may be engaged to facilitate improvements in performance that generalize to many different
tasks. Experiments will test the hypothesis that stable population-level neural activity emerges to strengthen
movement-related activity while minimizing non-task-related neural variability. Finally, participants will undergo
targeted neural training to determine if motor learning can be accelerated and whether different mechanisms of
neural reorganization are engaged in response to interventions that challenge the speed and accuracy properties
of the decoder in different ways. This project will improve our understanding of neural plasticity mechanisms
during extended BCI use in a real-world environment. Ultimately this knowledge will enable stable, high-
functioning BCI performance during independent home-use, which is critical for clinical translation.
皮质内脑机接口(bci)的性能在过去有了很大的进步
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jennifer L. Collinger其他文献
Use of Cortical Surface Stimulation towards Reliable Sensation in Human
- DOI:
10.1016/j.apmr.2015.10.071 - 发表时间:
2015-12-01 - 期刊:
- 影响因子:
- 作者:
Shivayogi V. Hiremath;Elizabeth C. Tyler-Kabara;Jesse Wheeler;Daniel W. Moran;Robert A. Gaunt;Jennifer L. Collinger;Stephen Thomas Foldes;Douglas John Weber;Weidong Chen;Michael Boninger;Wei Wang - 通讯作者:
Wei Wang
Evoking stable and precise tactile sensations via multi-electrode intracortical microstimulation of the somatosensory cortex
通过体感皮层的多电极皮层内微刺激唤起稳定而精确的触觉感受
- DOI:
10.1038/s41551-024-01299-z - 发表时间:
2024-12-06 - 期刊:
- 影响因子:26.600
- 作者:
Charles M. Greenspon;Giacomo Valle;Natalya D. Shelchkova;Taylor G. Hobbs;Ceci Verbaarschot;Thierri Callier;Ev I. Berger-Wolf;Elizaveta V. Okorokova;Brianna C. Hutchison;Efe Dogruoz;Anton R. Sobinov;Patrick M. Jordan;Jeffrey M. Weiss;Emily E. Fitzgerald;Dillan Prasad;Ashley Van Driesche;Qinpu He;Fang Liu;Robert F. Kirsch;Jonathan P. Miller;Ray C. Lee;David Satzer;Jorge Gonzalez-Martinez;Peter C. Warnke;Abidemi B. Ajiboye;Emily L. Graczyk;Michael L. Boninger;Jennifer L. Collinger;John E. Downey;Lee E. Miller;Nicholas G. Hatsopoulos;Robert A. Gaunt;Sliman J. Bensmaia - 通讯作者:
Sliman J. Bensmaia
Jennifer L. Collinger的其他文献
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{{ truncateString('Jennifer L. Collinger', 18)}}的其他基金
Quantifying neural variability and learning during real world brain-computer interface use
量化现实世界脑机接口使用过程中的神经变异和学习
- 批准号:
10838152 - 财政年份:2023
- 资助金额:
$ 53.27万 - 项目类别:
Development of an EMG-controlled BCI for biomimetic control of hand movement in humans
开发 EMG 控制的 BCI,用于仿生控制人类手部运动
- 批准号:
10651404 - 财政年份:2023
- 资助金额:
$ 53.27万 - 项目类别:
The interplay between kinematic and force representations in motor and somatosensory cortices during reaching, grasping, and object transport
伸手、抓握和物体运输过程中运动和体感皮层运动学和力表征之间的相互作用
- 批准号:
10546486 - 财政年份:2022
- 资助金额:
$ 53.27万 - 项目类别:
Quantifying neural variability and learning during real world brain-computer interface use
量化现实世界脑机接口使用过程中的神经变异和学习
- 批准号:
10363903 - 财政年份:2022
- 资助金额:
$ 53.27万 - 项目类别:
Influence of Task Complexity and Sensory Feedback on Cortical Control of Grasp Force
任务复杂性和感觉反馈对皮质控制握力的影响
- 批准号:
10705074 - 财政年份:2021
- 资助金额:
$ 53.27万 - 项目类别:
Influence of task complexity and sensory feedback on cortical control of grasp force
任务复杂性和感觉反馈对皮质控制抓握力的影响
- 批准号:
10289762 - 财政年份:2021
- 资助金额:
$ 53.27万 - 项目类别:
Influence of task complexity and sensory feedback on cortical control of grasp force
任务复杂性和感觉反馈对皮质控制抓握力的影响
- 批准号:
10480085 - 财政年份:2021
- 资助金额:
$ 53.27万 - 项目类别:
Eighth International Brain Computer Interface Meeting
第八届国际脑机接口会议
- 批准号:
9913702 - 财政年份:2020
- 资助金额:
$ 53.27万 - 项目类别:
Context-dependent processing in sensorimotor cortex
感觉运动皮层的上下文相关处理
- 批准号:
9791028 - 财政年份:2018
- 资助金额:
$ 53.27万 - 项目类别:
Investigation of Cortical Changes Following Spinal Cord Injury
脊髓损伤后皮质变化的调查
- 批准号:
8200932 - 财政年份:2012
- 资助金额:
$ 53.27万 - 项目类别:
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