Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain–Computer Interfaces

Even-Chen, Nir; Stavisky, Sergey D.; Pandarinath, Chethan; Nuyujukian, Paul; Blabe, Christine H; Hochberg, Leigh R.; Henderson, Jaimie M.; Shenoy, Krishna V.

doi:10.1109/tbme.2017.2776204

Cited by 13 publications

(6 citation statements)

References 73 publications

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“…T6 had a single 96-electrode Blackrock array (1 mm electrode length) placed in the same area. Figure 1 of 49 shows the exact locations of the arrays for both participants. Participant T5’s Grid Task data (Fig.…”

Section: Methodsmentioning

confidence: 99%

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Stavisky

Kao

Nuyujukian

et al. 2018

Sci Rep

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Brain-machine interfaces (BMIs) that decode movement intentions should ignore neural modulation sources distinct from the intended command. However, neurophysiology and control theory suggest that motor cortex reflects the motor effector’s position, which could be a nuisance variable. We investigated motor cortical correlates of BMI cursor position with or without concurrent arm movement. We show in two monkeys that subtracting away estimated neural correlates of position improves online BMI performance only if the animals were allowed to move their arm. To understand why, we compared the neural variance attributable to cursor position when the same task was performed using arm reaching, versus arms-restrained BMI use. Firing rates correlated with both BMI cursor and hand positions, but hand positional effects were greater. To examine whether BMI position influences decoding in people with paralysis, we analyzed data from two intracortical BMI clinical trial participants and performed an online decoder comparison in one participant. We found only small motor cortical correlates, which did not affect performance. These results suggest that arm movement and proprioception are the major contributors to position-related motor cortical correlates. Cursor position visual feedback is therefore unlikely to affect the performance of BMI-driven prosthetic systems being developed for people with paralysis.

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Section: Methodsmentioning

confidence: 99%

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Stavisky

Kao

Nuyujukian

et al. 2018

Sci Rep

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“…The review found that the use of neural networks to develop computational architectures is oriented toward the design of the networks, followed by learning algorithms to simulate different brain functions in 38.1% [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. Next, the development of brain simulation software is 14.3% [ 49 , 50 , 51 ], and the development of hybrid architectures (using brain computing interfaces supported by neuromorphic processors) accounts for 14.3% [ 52 , 53 , 54 ]. The development and improvement of brain computing interfaces was 9.5% [ 55 , 56 ], as was analysis and database storage through machine learning [ 57 , 58 ].…”

Section: Methods and Resultsmentioning

confidence: 99%

Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units

Hernández

Barbosa-Santillán

2022

Brain Sciences

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The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain–computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.

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“…Because the UNP user presented in this work has sensors implanted over both M1 and dorsolateral pre-frontal cortex (dLPFC), we can investigate both areas for error responses. While the dLPFC has been associated with error processing in the brain [14], to the best of our knowledge, this work provides the first investigation of ErrPs in dLPFC.…”

Section: Problem Formulationmentioning

confidence: 95%

“…Finally, because the previously reported ERNRs using ECoG [12] and local motor potential and spike-rate 'putative task outcome' signals using needle arrays [14] were over sensorimotor cortex and also not potential response features, it is difficult to relate the dLPFC bErrP to this work. It is noteworthy that we saw no marked difference in spectral response to FPs in the M1 bi-polar electrode, despite the fact its spectral response was highly correlated to the feedback (i.e., it was used to produce clicks and overall task performance was 83%).…”

Section: Neural Origins Of the Unp Berrp Signalmentioning

confidence: 99%

The dorsolateral pre-frontal cortex bi-polar error-related potential in a locked-in patient implanted with a daily use brain–computer interface

Freudenburg

Kohneshin

Aarnoutse

et al. 2021

Control Theory Technol.

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While brain computer interfaces (BCIs) offer the potential of allowing those suffering from loss of muscle control to once again fully engage with their environment by bypassing the affected motor system and decoding user intentions directly from brain activity, they are prone to errors. One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions. Error-related potentials (ErrPs) are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions. Here, we investigate the brain signals of an implanted BCI user suffering from locked-in syndrome (LIS) due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate, for the presence of error-related signals. We first establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex (dLPFC) in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate. Then, we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task. This work represents a first step toward detecting ErrPs during the daily home use of a communications BCI.

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Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain–Computer Interfaces

Abstract: These offline results suggest that it will be possible to improve the performance of clinical intracortical BCIs by incorporating a real-time error detect-and-undo system alongside the decoding of movement intention.

Cited by 13 publications

References 73 publications

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units

The dorsolateral pre-frontal cortex bi-polar error-related potential in a locked-in patient implanted with a daily use brain–computer interface

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