ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings

O’Shea, Daniel J.; Shenoy, Krishna V.

doi:10.1088/1741-2552/aaa365

Cited by 41 publications

(29 citation statements)

References 79 publications

(112 reference statements)

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“…long-time scale fluctuations in calcium fluorescence shared across many neurons due to bleaching or other factors, we used PCA to identify large variance PCs (≥ 5% total variance) reflecting these artifacts. Cell activity was then reconstructed using these PCs excluded from reconstruction (O’Shea and Shenoy, 2018). This method was applied only to data for mouse 1 in the rat exposure assay.…”

Section: Methodsmentioning

confidence: 99%

Dorsal Periaqueductal gray ensembles represent approach and avoidance states

Reis¹,

Lee²,

Maesta‐Pereira³

et al. 2020

Preprint

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Animals must balance needs to approach threats for risk-assessment and to avoid danger. The dorsal periaqueductal gray (dPAG) controls defensive behaviors, but it is unknown how it represents states associated with threat approach and avoidance. We identified a dPAG threat-avoidance ensemble in mice that showed higher activity far from threats such as the open arms of the elevated plus maze and a live predator. These cells were also more active during threat-avoidance behaviors such as escape and freezing, even though these behaviors have antagonistic motor output. Conversely, the threat-approach ensemble was more active during risk-assessment behaviors and near threats. Furthermore, unsupervised methods showed approach/avoidance states were encoded with shared activity patterns across threats. Lastly, the relative number of cells in each ensemble predicted threat-avoidance across mice. Thus, dPAG ensembles dynamically encode threat approach and avoidance states, providing a flexible mechanism to balance risk-assessment and danger avoidance.

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Section: Methodsmentioning

confidence: 99%

Dorsal Periaqueductal gray ensembles represent approach and avoidance states

Reis¹,

Lee²,

Maesta‐Pereira³

et al. 2020

Preprint

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“…Comparison among different strategies of stimulation of PNS to evoke tactile percepts. Parameters that have been considered are: i) number of subjects (S) and implant duration; ii) type of implanted electrode, number of channels, nerve target; iii) impedance measurement; iv) type of stimulation pattern; v) threshold charge/current over time; vi) …”

Section: Implantable Neuroprostheses In Clinical Applicationsmentioning

confidence: 99%

“…Plexon offers solutions data acquisition and stimulation systems but also electrodes for research needs for cortical recordings (channel counts up to 32 with platinum–iridium contacts) …”

Section: Future Of Neuroprostheticsmentioning

confidence: 99%

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Cutrone

Micera

2019

Adv Healthcare Materials

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functions of nervous system when damages occur. Moreover, in case of physical impairments, the use of artificial tactile sensors is also paramount to ensure the possibility to restore the sense of touch to the patient. Bioinspiration from human natural touch could help in the release of enhanced tactile sensors to be used in neuroprosthetics. Technological and biological advancements brought serious improvements in the quality of these devices, such as miniaturization and increased biocompatibility. This article reviews the essential design criteria, working principles, materials, and technical considerations on neural interfaces and tactile sensors; their clinical application in neuroprosthetics and their future progress toward optimized solutions. Neural InterfacesA neural interface device (NID) is an implantable tool that aims at restoring nervous system functions in case of impairments or communicates with external components (e.g., limb prostheses); its main objective is to record neural signal from a small group of axons/cells or to stimulate a selected area of the nervous system. The clinical potentialities of NIDs range from their applications in central nervous system (CNS) for treating epilepsy seizures, mitigating Parkinson's disease; in peripheral nervous system (PNS) for controlling limb prostheses in amputees and restore sensory feedback; in autonomous nervous system such as vagus nerve to treat drug-resistant epilepsy and chronic depression to finally auditory and vision systems to restore the perception of specific sounds and phosphenes. An ideal NID should be biocompatible, highly selective, low invasive and stable over long time. If intended for clinical applications, the functionality of a NID should be reliable and should last decades. Considering the different implantation sites of interest of the device, various solutions have been proposed to optimize the communication between the NID and the surrounding environment. Figure 1 highlights the main applications of most common used NIDs in neuroprosthetics. With a special focus on implantable neuroprostheses and motor system, this paragraph will portray the design criteria, the materials and technical considerations heretofore used for the development of diverse types of NID used in CNS and PNS.Neuroprosthetics and neuromodulation represent a promising field for several related applications in the central and peripheral nervous system, such as the treatment of neurological disorders, the control of external robotic devices, and the restoration of lost tactile functions. These actions are allowed by the neural interface, a miniaturized implantable device that most commonly exploits electrical energy to fulfill these operations. A neural interface must be biocompatible, stable over time, low invasive, and highly selective; the challenge is to develop a safe, compact, and reliable tool for clinical applications. In case of anatomical impairments, neuroprosthetics is bound to the need of exploring the surrounding environment by fast-responsive and ...

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“…This involves (1) using recorded neural responses to control the hand, (2) stimulating somatosensory neurons to provide tactile and proprioceptive feedback, and (3) ensuring that stimulation artifacts do not corrupt the recorded signals being used to control the hand. Several artifact reduction methods have been proposed for (3) -we refer the reader to [32,33,34]. We focus here on combining (1) decoding with (2) encoding.…”

Section: Closed-loop Prosthetic Controlmentioning

confidence: 99%

Towards neural co-processors for the brain: combining decoding and encoding in brain–computer interfaces

Rao

2019

Current Opinion in Neurobiology

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The field of brain-computer interfaces is poised to advance from the traditional goal of controlling prosthetic devices using brain signals to combining neural decoding and encoding within a single neuroprosthetic device. Such a device acts as a "co-processor" for the brain, with applications ranging from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory. We review recent progress in simultaneous decoding and encoding for closed-loop control and plasticity induction. To address the challenge of multi-channel decoding and encoding, we introduce a unifying framework for developing brain co-processors based on artificial neural networks and deep learning. These "neural co-processors" can be used to jointly optimize cost functions with the nervous system to achieve desired behaviors ranging from targeted neuro-rehabilitation to augmentation of brain function.

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ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings

Abstract: We hope that enabling simultaneous electrical stimulation and multielectrode array recording will help elucidate the causal links between neural activity and cognition and facilitate naturalistic sensory protheses.

Cited by 41 publications

References 79 publications

Dorsal Periaqueductal gray ensembles represent approach and avoidance states

Dorsal Periaqueductal gray ensembles represent approach and avoidance states

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Towards neural co-processors for the brain: combining decoding and encoding in brain–computer interfaces

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