scholarly journals Explant Analysis of Utah Electrode Arrays Implanted in Human Cortex for Brain-Computer-Interfaces

2021 ◽  
Vol 9 ◽  
Author(s):  
Kevin Woeppel ◽  
Christopher Hughes ◽  
Angelica J. Herrera ◽  
James R. Eles ◽  
Elizabeth C. Tyler-Kabara ◽  
...  
Keyword(s):  
Therapeutic Potential ◽  
Metal Layer ◽  
Initial Increase ◽  
Brain Computer Interfaces ◽  
Clinical Implementation ◽  
Electrode Arrays ◽  
Material Degradation ◽  
Material Stability ◽  
Computer Interfaces ◽  
Explant Analysis

Brain-computer interfaces are being developed to restore movement for people living with paralysis due to injury or disease. Although the therapeutic potential is great, long-term stability of the interface is critical for widespread clinical implementation. While many factors can affect recording and stimulation performance including electrode material stability and host tissue reaction, these factors have not been investigated in human implants. In this clinical study, we sought to characterize the material integrity and biological tissue encapsulation via explant analysis in an effort to identify factors that influence electrophysiological performance. We examined a total of six Utah arrays explanted from two human participants involved in intracortical BCI studies. Two platinum (Pt) arrays were implanted for 980 days in one participant (P1) and two Pt and two iridium oxide (IrOx) arrays were implanted for 182 days in the second participant (P2). We observed that the recording quality followed a similar trend in all six arrays with an initial increase in peak-to-peak voltage during the first 30–40 days and gradual decline thereafter in P1. Using optical and two-photon microscopy we observed a higher degree of tissue encapsulation on both arrays implanted for longer durations in participant P1. We then used scanning electron microscopy and energy dispersive X-ray spectroscopy to assess material degradation. All measures of material degradation for the Pt arrays were found to be more prominent in the participant with a longer implantation time. Two IrOx arrays were subjected to brief survey stimulations, and one of these arrays showed loss of iridium from most of the stimulated sites. Recording performance appeared to be unaffected by this loss of iridium, suggesting that the adhesion of IrOx coating may have been compromised by the stimulation, but the metal layer did not detach until or after array removal. In summary, both tissue encapsulation and material degradation were more pronounced in the arrays that were implanted for a longer duration. Additionally, these arrays also had lower signal amplitude and impedance. New biomaterial strategies that minimize fibrotic encapsulation and enhance material stability should be developed to achieve high quality recording and stimulation for longer implantation periods.

scholarly journals Explant analysis of Utah electrode arrays implanted in human cortex for brain-computer-interfaces

2021 ◽  
Author(s):  
Kevin Woeppel ◽  
Christopher Hughes ◽  
Angelica J. Herrera ◽  
James Eles ◽  
Elizabeth C. Tyler-Kabara ◽  
...  
Keyword(s):  
Therapeutic Potential ◽  
Metal Layer ◽  
Initial Increase ◽  
Brain Computer Interfaces ◽  
Clinical Implementation ◽  
Electrode Arrays ◽  
Material Degradation ◽  
Material Stability ◽  
Computer Interfaces ◽  
Explant Analysis

AbstractBrain-computer interfaces are being developed to restore movement for people living with paralysis due to injury or disease. Although the therapeutic potential is great, long-term stability of the interface is critical for widespread clinical implementation. While many factors can affect recording and stimulation performance including electrode material stability and host tissue reaction, these factors have not been investigated in human implants. In this clinical study, we sought to characterize the material integrity and biological tissue encapsulation via explant analysis in an effort to identify factors that influence electrophysiological performance.We examined a total of six Utah arrays explanted from two human participants involved in intracortical BCI studies. Two Pt arrays were implanted for 980 days in one participant (P1) and two Pt and two iridium oxide (IrOx) arrays were implanted for 182 days in the second participant (P2). We observed that the recording quality followed a similar trend in all 6 arrays with an initial increase in peak-to-peak voltage during the first 30-40 days and gradual decline thereafter in P1.Using optical and two-photon microscopy (TPM) we observed a higher degree of tissue encapsulation on both arrays implanted for longer durations in participant P1. We then used scanning electron microscopy and energy dispersive X-ray spectroscopy to assess material degradation. All measures of material degradation for the Pt arrays were found to be more prominent in the participant with a longer implantation time. Two IrOx arrays were subjected to brief survey stimulations, and one of these arrays showed loss of iridium from majority of the stimulated sites. Recording performance appeared to be unaffected by this loss of iridium, suggesting that the adhesion of IrOx coating may have been compromised by the stimulation, but the metal layer did not detach until or after array removal.In summary, both tissue encapsulation and material degradation were more pronounced in the arrays that were implanted for a longer duration. Additionally, these arrays also had lower signal amplitude and impedance. New biomaterial strategies that minimize fibrotic encapsulation and enhance material stability should be developed to achieve high quality recording and stimulation for longer implantation periods.

scholarly journals The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain-Computer Interface Platform

2022 ◽  
Author(s):  
Elton Ho ◽  
Mark Hettick ◽  
Demetrios Papageorgiou ◽  
Adam J Poole ◽  
Manuel Monge ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Neural Interface ◽  
Cortical Surface ◽  
Brain Computer Interfaces ◽  
Electrode Arrays ◽  
Disease States ◽  
Functional Regions ◽  
Minimally Invasive Surgical ◽  
Computer Interfaces ◽  
Novel Approach

Progress toward the development of brain-computer interfaces has signaled the potential to restore, replace, or augment lost or impaired neurological function in a variety of disease states. Existing brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes, which limit addressable applications of the technology and the number of eligible patients. Here we describe a novel approach to constructing a neural interface, comprising conformable thin-film electrode arrays and a minimally invasive surgical delivery system that together facilitate communication with large portions of the cortical surface in bidirectional fashion (enabling both recording and stimulation). We demonstrate the safety and feasibility of rapidly delivering reversible implants containing over 2,000 microelectrodes to multiple functional regions in both hemispheres of the Gottingen minipig brain simultaneously, without requiring a craniotomy, at an effective insertion rate faster than 40 ms per channel, without damaging the cortical surface. We further demonstrate the performance of this system for high-density neural recording, focal cortical stimulation, and accurate neural decoding. Such a system promises to accelerate efforts to better decode and encode neural signals, and to expand the patient population that could benefit from neural interface technology.

scholarly journals Generative adversarial neural networks maintain decoder accuracy during signal disruption in simulated long-term recordings from brain computer interfaces

2021 ◽  
Author(s):  
Thomas Stephens ◽  
Jon Cafaro ◽  
Ryan MacRae ◽  
Stephen B Simons
Keyword(s):  
Neural Networks ◽  
Neural Interfaces ◽  
Time Signal ◽  
Signal Recovery ◽  
Brain Computer Interfaces ◽  
Electrode Arrays ◽  
Generative Adversarial Network ◽  
Computer Interfaces ◽  
Over Time

Chronically implanted brain-computer interfaces (BCIs) provide amazing opportunities to those living with disability and for the treatment of chronic disorders of the nervous system. However, this potential has yet to be fully realized in part due to the lack of stability in measured signals over time. Signal disruption stems from multiple sources including mechanical failure of the interface, changes in neuron health, and glial encapsulation of the electrodes that alter the impedance. In this study we present an algorithmic solution to the problem of long-term signal disruption in chronically implanted neural interfaces. Our approach utilizes a generative adversarial network (GAN), based on the original Unsupervised Image to Image Translation (UNIT) algorithm, which learns how to recover degraded signals back to their analogous non-disrupted (clean) exemplars measured at the time of implant. We demonstrate that this approach can reliably recover simulated signals in two types of commonly used neural interfaces: multi-electrode arrays (MEA), and electrocorticography (ECoG). To test the accuracy of signal recovery we employ a common BCI paradigm wherein a classification algorithm (neural decoder) is trained on the starting (non-disrupted) set of signals. Performance of the decoder demonstrates expected failure over time as the signal disruption accumulates. In simulated MEA experiments, our approach recovers decoder accuracy to >90% when as many as 13/ 32 channels are lost, or as many as 28/32 channels have their neural responses altered. In simulated ECoG experiments, our approach shows stabilization of the neural decoder indefinitely with decoder accuracies >95% over simulated lifetimes of over 1 year. Our results suggest that these types of neural networks can provide a useful tool to improve the long-term utility of chronically implanted neural interfaces.

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

scholarly journals Privacy and brain-computer interfaces

2016 ◽  
Vol 46 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Kirsten Wahlstrom ◽  
N. Ben Fairweather ◽  
Helen Ashman
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 560
Author(s):  
Andrea Bonci ◽  
Simone Fiori ◽  
Hiroshi Higashi ◽  
Toshihisa Tanaka ◽  
Federica Verdini
Keyword(s):  
Biomedical Engineering ◽  
Data Privacy ◽  
Lie Detection ◽  
Human Cognition ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Ethical And Legal Issues ◽  
And Performance ◽  
Brain Computer Interfacing

The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

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