Parsing learning in networks using brain–machine interfaces

Orsborn, Amy L.; Pesaran, Bijan

doi:10.1016/j.conb.2017.08.002

Cited by 46 publications

(32 citation statements)

References 47 publications

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“…OPEN ACCESS direction as the monkey performed the task. This pipeline highlights the potential to incorporate calcium imaging methods into closed-loop approaches for interrogating neural function (Orsborn and Pesaran, 2017). However, additional advances will be needed to optimize decoding algorithms to calcium signals, the temporal statistics of which differ greatly from those of conventional electrophysiological signals.…”

Section: Llmentioning

confidence: 99%

Windows and periscopes into primate behavior

Canfield

Orsborn²,

Horwitz³

2021

Cell Reports

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Section: Llmentioning

confidence: 99%

Windows and periscopes into primate behavior

Canfield

Orsborn²,

Horwitz³

2021

Cell Reports

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“…Motor BMIs use a decoder to convert neural data to a control signal, such as moving a computer cursor. Closed-loop BMIs form a closed control loop via visual feedback, creating novel sensorimotor systems that can be manipulated to study neural functions [4].…”

Section: A Motivating Backgroundmentioning

confidence: 99%

A Game-Theoretic Model for Co-Adaptive Brain-Machine Interfaces

Madduri

Burden

Orsborn

2020

Preprint

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Co-adaptation in brain-machine interfaces (BMIs) can improve performance and facilitate user learning. We propose and analyze a mathematical model for co-adaptation in BMIs. We model the brain and the decoder as strategic agents who seek to minimize their individual cost functions, leading to a game-theoretic formulation of interaction. We frame our BMI model as a potential game to identify stationary points (Nash equilibria) of the brain-decoder interactions, which correspond to points at which both the brain and the decoder stop adapting. Assuming the brain and the decoder adapt using gradientbased schemes, we analytically show how convergence to these equilibria depends on agent learning rates. This theoretical framework presents a basis for simulating co-adaption using dynamic game theory and can be extended to tasks with multiple dimensions and to different decoder models. This framework can ultimately be used to inform adaptive decoder design to shape brain learning and optimize BMI performance.

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“…However, the threshold selection is generally far from being an automated procedure for big datasets [ 29 ]. Undeterred by the promising evidence, there is a need to understand the learning mechanisms and the brain network reorganization, aiming to support the efficiency of BCI systems [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators

Caicedo-Acosta

Castaño

Acosta-Medina

et al. 2021

Sensors

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Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing’s neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability.

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Parsing learning in networks using brain–machine interfaces

Cited by 46 publications

References 47 publications

Windows and periscopes into primate behavior

Windows and periscopes into primate behavior

A Game-Theoretic Model for Co-Adaptive Brain-Machine Interfaces

Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators

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