scholarly journals An Integrated Brain-Machine Interface Platform With Thousands of Channels

10.2196/16194 ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. e16194 ◽  
Author(s):  
Elon Musk ◽  
Keyword(s):  
Electrode Array ◽  
Brain Regions ◽  
White Paper ◽  
Brain Machine Interface ◽  
Data Streaming ◽  
Brain Machine Interfaces ◽  
Implanted Electrodes ◽  
High Bandwidth ◽  
Machine Interface ◽  
Neurosurgical Robot

Brain-machine interfaces hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical brain-machine interfaces have not yet been widely adopted, in part, because modest channel counts have limited their potential. In this white paper, we describe Neuralink’s first steps toward a scalable high-bandwidth brain-machine interface system. We have built arrays of small and flexible electrode “threads,” with as many as 3072 electrodes per array distributed across 96 threads. We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute. Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small implantable device that contains custom chips for low-power on-board amplification and digitization: The package for 3072 channels occupies less than 23×18.5×2 mm3. A single USB-C cable provides full-bandwidth data streaming from the device, recording from all channels simultaneously. This system has achieved a spiking yield of up to 70% in chronically implanted electrodes. Neuralink’s approach to brain-machine interface has unprecedented packaging density and scalability in a clinically relevant package.

scholarly journals An integrated brain-machine interface platform with thousands of channels

2019 ◽  
Author(s):  
Elon Musk ◽  
Keyword(s):  
Electrode Array ◽  
Brain Regions ◽  
White Paper ◽  
Data Streaming ◽  
Flexible Electrode ◽  
Implanted Electrodes ◽  
High Bandwidth ◽  
Machine Interface ◽  
The Brain ◽  
Neurosurgical Robot

AbstractBrain-machine interfaces (BMIs) hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical BMIs have not yet been widely adopted, in part because modest channel counts have limited their potential. In this white paper, we describe Neuralink’s first steps toward a scalable high-bandwidth BMI system. We have built arrays of small and flexible electrode “threads”, with as many as 3,072 electrodes per array distributed across 96 threads. We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute. Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small implantable device that contains custom chips for low-power on-board amplification and digitization: the package for 3,072 channels occupies less than (23 × 18.5 × 2) mm3. A single USB-C cable provides full-bandwidth data streaming from the device, recording from all channels simultaneously. This system has achieved a spiking yield of up to 70% in chronically implanted electrodes. Neuralink’s approach to BMI has unprecedented packaging density and scalability in a clinically relevant package.

scholarly journals An Integrated Brain-Machine Interface Platform With Thousands of Channels (Preprint)

2019 ◽  
Author(s):  
Elon Musk ◽  
Keyword(s):  
Electrode Array ◽  
Brain Regions ◽  
White Paper ◽  
Data Streaming ◽  
Flexible Electrode ◽  
Implanted Electrodes ◽  
High Bandwidth ◽  
Machine Interface ◽  
The Brain ◽  
Neurosurgical Robot

UNSTRUCTURED Brain-machine interfaces (BMIs) hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical BMIs have not yet been widely adopted, in part, because modest channel counts have limited their potential. In this white paper, we describe Neuralink’s first steps toward a scalable high-bandwidth BMI system. We have built arrays of small and flexible electrode “threads,” with as many as 3072 electrodes per array distributed across 96 threads. We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute. Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small implantable device that contains custom chips for low-power on-board amplification and digitization: The package for 3072 channels occupies less than 23×18.5×2 mm3. A single USB-C cable provides full-bandwidth data streaming from the device, recording from all channels simultaneously. This system has achieved a spiking yield of up to 70% in chronically implanted electrodes. Neuralink’s approach to BMI has unprecedented packaging density and scalability in a clinically relevant package.

scholarly journals The Reconnecting the Hand and Arm with Brain (ReHAB) Commentary on “An Integrated Brain-Machine Interface Platform With Thousands of Channels” (Preprint)

2019 ◽  
Author(s):  
Robert F Kirsch ◽  
A Bolu Ajiboye ◽  
Jonathan P Miller
Keyword(s):  
Neurological Disorders ◽  
Brain Machine Interface ◽  
Loss Of Function ◽  
Brain Machine Interfaces ◽  
Electrode Interface ◽  
Machine Interface ◽  
Almost All ◽  
Neuroprosthetic Devices ◽  
The Brain

UNSTRUCTURED Intracortical brain-machine interfaces are a promising technology for allowing people with chronic and severe neurological disorders that resulted in loss of function to potentially regain those functions through neuroprosthetic devices. The penetrating microelectrode arrays used in almost all previous studies of intracortical brain-machine interfaces in people had a limited recording life (potentially due to issues with long-term biocompatibility), as well as a limited number of recording electrodes with limited distribution in the brain. Significant advances are required in this array interface to deal with the issues of long-term biocompatibility and lack of distributed recordings. The Musk and Neuralink manuscript proposes a novel and potentially disruptive approach to advancing the brain-electrode interface technology, with the potential of addressing many of these hurdles. Our commentary addresses the potential advantages of the proposed approach, as well as the remaining challenges to be addressed.

scholarly journals Real-Time Linear Prediction of Simultaneous and Independent Movements of Two Finger Groups Using an Intracortical Brain-Machine Interface

2020 ◽  
Author(s):  
Samuel R. Nason ◽  
Matthew J. Mender ◽  
Alex K. Vaskov ◽  
Matthew S. Willsey ◽  
Parag G. Patil ◽  
...  
Keyword(s):  
Real Time ◽  
Neural Activity ◽  
Cortical Neurons ◽  
High Speed ◽  
Neural Prostheses ◽  
Brain Machine Interface ◽  
Brain Machine Interfaces ◽  
Individual Finger ◽  
Machine Interface ◽  
Group Movements

SUMMARYModern brain-machine interfaces can return function to people with paralysis, but current hand neural prostheses are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a novel task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined, presenting separate targets for each group. During online brain control, the ReFIT Kalman filter demonstrated the capability of individuating movements of each finger group with high performance, enabling a nonhuman primate to acquire two targets simultaneously at 1.95 targets per second, resulting in an average information throughput of 2.1 bits per second. To understand this result, we performed single unit tuning analyses. Cortical neurons were active for movements of an individual finger group, combined movements of both finger groups, or both. Linear combinations of neural activity representing individual finger group movements predicted the neural activity during combined finger group movements with high accuracy, and vice versa. Hence, a linear model was able to explain how cortical neurons encode information about multiple dimensions of movement simultaneously. Additionally, training ridge regressing decoders with independent component movements was sufficient to predict untrained higher-complexity movements. Our results suggest that linear decoders for brain-machine interfaces may be sufficient to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.

scholarly journals Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5528
Author(s):  
Peng Zhang ◽  
Lianying Chao ◽  
Yuting Chen ◽  
Xuan Ma ◽  
Weihua Wang ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Brain Machine Interface ◽  
Brain Machine Interfaces ◽  
Neural Data ◽  
Neural Recordings ◽  
New Knowledge ◽  
Machine Interface

Background: For the nonstationarity of neural recordings in intracortical brain–machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders. Methods: To solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures. Results: The decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times. Conclusions: This paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice.

scholarly journals Towards Autonomous Intra-cortical Brain Machine Interfaces: Applying Bandit Algorithms for Online Reinforcement Learning

2020 ◽  
Author(s):  
Shoeb Shaikh ◽  
Rosa So ◽  
Tafadzwa Sibindi ◽  
Camilo Libedinsky ◽  
Arindam Basu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Discrete Control ◽  
Brain Machine Interface ◽  
Control Task ◽  
Brain Machine Interfaces ◽  
Discrete State ◽  
Supervised Methods ◽  
Machine Interface

AbstractThis paper presents application of Banditron - an online reinforcement learning algorithm (RL) in a discrete state intra-cortical Brain Machine Interface (iBMI) setting. We have analyzed two datasets from non-human primates (NHPs) - NHP A and NHP B each performing a 4-option discrete control task over a total of 8 days. Results show average improvements of ≈ 15%, 6% in NHP A and 15%, 21% in NHP B over state of the art algorithms - Hebbian Reinforcement Learning (HRL) and Attention Gated Reinforcement Learning (AGREL) respectively. Apart from yielding a superior decoding performance, Banditron is also the most computationally friendly as it requires two orders of magnitude less multiply-and-accumulate operations than HRL and AGREL. Furthermore, Banditron provides average improvements of at least 40%, 15% in NHPs A, B respectively compared to popularly employed supervised methods - LDA, SVM across test days. These results pave the way towards an alternate paradigm of temporally robust hardware friendly reinforcement learning based iBMIs.

scholarly journals The Reconnecting the Hand and Arm with Brain (ReHAB) Commentary on “An Integrated Brain-Machine Interface Platform With Thousands of Channels”

10.2196/16339 ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. e16339 ◽  
Author(s):  
Robert F Kirsch ◽  
A Bolu Ajiboye ◽  
Jonathan P Miller
Keyword(s):  
Neurological Disorders ◽  
Brain Machine Interface ◽  
Loss Of Function ◽  
Brain Machine Interfaces ◽  
Electrode Interface ◽  
Machine Interface ◽  
Almost All ◽  
Neuroprosthetic Devices ◽  
The Brain

Intracortical brain-machine interfaces are a promising technology for allowing people with chronic and severe neurological disorders that resulted in loss of function to potentially regain those functions through neuroprosthetic devices. The penetrating microelectrode arrays used in almost all previous studies of intracortical brain-machine interfaces in people had a limited recording life (potentially due to issues with long-term biocompatibility), as well as a limited number of recording electrodes with limited distribution in the brain. Significant advances are required in this array interface to deal with the issues of long-term biocompatibility and lack of distributed recordings. The Musk and Neuralink manuscript proposes a novel and potentially disruptive approach to advancing the brain-electrode interface technology, with the potential of addressing many of these hurdles. Our commentary addresses the potential advantages of the proposed approach, as well as the remaining challenges to be addressed.

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