Neural Plasticity in Sensorimotor Brain–Machine Interfaces

Dadarlat, Maria C.; Canfield, Ryan A.; Orsborn, Amy L.

doi:10.1146/annurev-bioeng-110220-110833

Cited by 7 publications

(4 citation statements)

References 160 publications

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“…One example of the impact of neural plasticity on subjective experience is in the field of sensory perception [103][104][105]. Prolonged exposure to certain sensory stimuli can lead to neural adaptation and perceptual learning.…”

Section: Neural Plasticity and Adaptationmentioning

confidence: 99%

Epistemic Challenges in Neurophenomenology: Exploring the Reliability of Knowledge and Its Ontological Implications

Shutaleva

2023

Philosophies

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This article investigates the challenges posed by the reliability of knowledge in neurophenomenology and its connection to reality. Neurophenomenological research seeks to understand the intricate relationship between human consciousness, cognition, and the underlying neural processes. However, the subjective nature of conscious experiences presents unique epistemic challenges in determining the reliability of the knowledge generated in this research. Personal factors such as beliefs, emotions, and cultural backgrounds influence subjective experiences, which vary from individual to individual. On the other hand, scientific knowledge aims to uncover universal truths based on empirical observations and objective principles. Reconciling the subjective and objective realms presents a significant challenge in determining the reliability of knowledge generated through neurophenomenological research. This article aims to examine the inherent limitations and challenges of neurophenomenological research to shed light on the complexities involved in understanding the nature of knowledge itself. This article highlights that the ontological implications of the reliability of knowledge in neurophenomenology arise from the question of how subjective experiences relate to objective reality. Understanding the neural correlates and mechanisms behind subjective experiences can provide insight into the underlying ontological nature of consciousness.

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Section: Neural Plasticity and Adaptationmentioning

confidence: 99%

Epistemic Challenges in Neurophenomenology: Exploring the Reliability of Knowledge and Its Ontological Implications

Shutaleva

2023

Philosophies

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“…Neural interfaces can restore or augment human capabilities (Serruya et al, 2002; Taylor et al, 2002; Carmena et al, 2003; Hochberg et al, 2006; Pandarinath and Bensmaia, 2022; Dadarlat et al, 2023). In a neural interface, signals from the user are translated via a decoder algorithm to control a device.…”

Section: Introductionmentioning

confidence: 99%

Predicting and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces

Madduri,

Yamagami,

et al. 2024

Preprint

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Neural interfaces can restore or augment human sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Leveraging user and decoder adaptation to create co-adaptive interfaces presents opportunities to improve usability and personalize devices. However, we lack principled methods to model and optimize the complex two-learner dynamics that arise in co-adaptive interfaces. Here, we present new computational methods based on control theory and game theory to analyze and generate predictions for user-decoder co-adaptive outcomes in continuous interactions. We tested these computational methods using an experimental platform where human participants (N=14) learn to control a cursor using an adaptive myoelectric interface to track a target on a computer display. Our framework predicted the outcome of co-adaptive interface interactions and revealed how interface properties can shape user behavior. These findings contribute new tools to design personalized, closed-loop, co-adaptive neural interfaces.

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“…Excessive beta band neural oscillations (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) in the cortex-BG-thalamus network have been widely observed under Parkinsonian conditions in rats [7], non-human primates [8], and human patients [9,10]. The suppression of excessive beta oscillations has been shown to be correlated with the alleviation of motor symptoms in PD [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Second, the network consists of complex neuronal interactions that make the network activity nonlinearly coupled [13,14]. Third, the network activity and interactions can further be affected by time-varying and non-stationary internal and external factors such as psychiatric state variations [15], circadian rhythm [16], and neural plasticity [17,18].…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics

Fang,

Berman,

Wang

et al. 2024

J. Neural Eng.

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Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics. Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system's sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods. Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods. Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.

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Neural Plasticity in Sensorimotor Brain–Machine Interfaces

Cited by 7 publications

References 160 publications

Epistemic Challenges in Neurophenomenology: Exploring the Reliability of Knowledge and Its Ontological Implications

Epistemic Challenges in Neurophenomenology: Exploring the Reliability of Knowledge and Its Ontological Implications

Predicting and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces

Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics

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