Bidirectional Brain-computer Interfaces Research Articles

Predicting lever press in a vibrotactile yes/no detection task from S1 cortex of freely behaving rats by µECoG arrays

Aim of the Study Brain-computer interfaces (BCIs) may help patients with severe neurological deficits communicate with the external world. Based on microelectrocorticography (µECoG) data recorded from the primary somatosensory cortex (S1) of unrestrained behaving rats, this study attempts to decode lever presses in a psychophysical detection task by using machine learning algorithms. Materials and Methods 16-channel Pt-Ir microelectrode arrays were implanted on the S1 of two rats, and µECoG was recorded during a vibrotactile yes/no detection task. For this task, the rats were trained to press the right lever when they detected the vibrotactile stimulus and the left lever when they did not. The multichannel µECoG data was analysed offline by time-frequency methods and its features were used for binary classification of the lever press at each trial. Several machine learning algorithms were tested as such. Results The psychophysical sensitivities (A') were similar and low for both rats (0.58). Rat 2 (B'': −0.11) had higher bias for the right lever than Rat 1 (B'': − 0.01). The lever presses could be predicted with accuracies over 66% with all the tested algorithms, and the highest average accuracy (78%) was with the support vector machine. Conclusion According to the recent studies, sensory feedback increases the benefit of the BCIs. The current proof-of-concept study shows that lever presses can be decoded from the S1; therefore, this area may be utilised for a bidirectional BCI in the future.

Suppression of cortical electrostimulation artifacts using pre-whitening and null projection

Objective. Invasive brain–computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods. Approach. We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects. Main results. In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%–80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement. Significance. PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.

Artifact propagation in subdural cortical electrostimulation: Characterization and modeling.

Cortical stimulation via electrocorticography (ECoG) may be an effective method for inducing artificial sensation in bi-directional brain-computer interfaces (BD-BCIs). However, strong electrical artifacts caused by electrostimulation may significantly degrade or obscure neural information. A detailed understanding of stimulation artifact propagation through relevant tissues may improve existing artifact suppression techniques or inspire the development of novel artifact mitigation strategies. Our work thus seeks to comprehensively characterize and model the propagation of artifacts in subdural ECoG stimulation. To this end, we collected and analyzed data from eloquent cortex mapping procedures of four subjects with epilepsy who were implanted with subdural ECoG electrodes. From this data, we observed that artifacts exhibited phase-locking and ratcheting characteristics in the time domain across all subjects. In the frequency domain, stimulation caused broadband power increases, as well as power bursts at the fundamental stimulation frequency and its super-harmonics. The spatial distribution of artifacts followed the potential distribution of an electric dipole with a median goodness-of-fit of R2 = 0.80 across all subjects and stimulation channels. Artifacts as large as ±1,100 μV appeared anywhere from 4.43 to 38.34 mm from the stimulation channel. These temporal, spectral and spatial characteristics can be utilized to improve existing artifact suppression techniques, inspire new strategies for artifact mitigation, and aid in the development of novel cortical stimulation protocols. Taken together, these findings deepen our understanding of cortical electrostimulation and provide critical design specifications for future BD-BCI systems.

PO113 / #853 BRAIN INTERCHANGE BCI NEURAL INTERFACING SYSTEM: NEURAL RECORDING AND STIMULATION USING MICRO-ECOG AND DBS ELECTRODES IN SHEEP: E-POSTER VIEWING

The rising use of active neural implants and bi-directional brain-computer interfaces (BCIs) in translational and investigational therapeutical research and medical applications is emerging in today's neuroscientific landscape. Fully implantable neural interfaces and BCI devices must provide a broad range of technical flexibility, long-term implantation potential, and medical safety to the user and to patients to meet the complex requirements for potential medical applications. The system employed here meets the aforementioned requirements: recording configuration choice, open- and closed-loop stimulation functionality, flexible electrode type choice, inherent safety features and biocompatible materials.

A CMOS Dual-Mode Brain-Computer Interface Chipset With 2-mV Precision Time-Based Charge Balancing and Stimulation-Side Artifact Suppression

This article presents a multipolar neural stimulation and mixed-signal neural data acquisition (DAQ) chipset for fully implantable bi-directional brain–computer interfaces (BD-BCIs). The stimulation system employs four 40 V compliant current-stimulators, each capable of sourcing/sinking a maximum 12.75 mA stimulation current, connected to 16 output channels through a high-voltage (HV) switch fabric. A novel time-based charge balancing (TBCB) technique is introduced to reduce the residual voltage on the electrode-electrolyte interface during the inter-pulse time interval, achieving 2 mV charge balancing precision. Additionally, an analytical study of the charge balancing accuracy for the proposed technique is provided. The recording system incorporates a dual-mode DAQ architecture that consists of a 32-element front-end array and a mixed-signal back-end including analog-to-digital converters (ADCs) for both training (i.e., full-band) and decoding (i.e., baseband) operations. Leveraging the flexibility of the multipolar operation, stimulation-side contour shaping (SSCS) artifact cancellation is adopted to significantly suppress stimulation artifacts by up to 45 dB. SSCS method prevents the recording front-ends from saturation and greatly relaxes the dynamic range requirement of the recording system, enabling a truly bi-directional operation. The prototype chipset is fabricated in an HV 180-nm CMOS process and demonstrates a significant performance improvement compared to the prior art.

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease.

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson's disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.

Perception of microstimulation frequency in human somatosensory cortex.

Microstimulation in the somatosensory cortex can evoke artificial tactile percepts and can be incorporated into bidirectional brain-computer interfaces (BCIs) to restore function after injury or disease. However, little is known about how stimulation parameters themselves affect perception. Here, we stimulated through microelectrode arrays implanted in the somatosensory cortex of two human participants with cervical spinal cord injury and varied the stimulus amplitude, frequency, and train duration. Increasing the amplitude and train duration increased the perceived intensity on all tested electrodes. Surprisingly, we found that increasing the frequency evoked more intense percepts on some electrodes but evoked less-intense percepts on other electrodes. These different frequency-intensity relationships were divided into three groups, which also evoked distinct percept qualities at different stimulus frequencies. Neighboring electrode sites were more likely to belong to the same group. These results support the idea that stimulation frequency directly controls tactile perception and that these different percepts may be related to the organization of somatosensory cortex, which will facilitate principled development of stimulation strategies for bidirectional BCIs.

Advancing sensory neuroprosthetics using artificial brain networks

Implementation of effective brain or neural stimulation protocols for restoration of complex sensory perception, e.g., in the visual domain, is an unresolved challenge. By leveraging the capacity of deep learning to model the brain’s visual system, optic nerve stimulation patterns could be derived that are predictive of neural responses of higher-level cortical visual areas in silico. This novel approach could be generalized to optimize different types of neuroprosthetics or bidirectional brain-computer interfaces (BCIs).

Technology Integration Methods for Bi-directional Brain-computer Interfaces and XR-based Interventions.

Brain stimulation therapies have been established as effective treatments for Parkinson’s disease, essential tremor, and epilepsy, as well as having high diagnostic and therapeutic potential in a wide range of neurological and psychiatric conditions. Novel interventions such as extended reality (XR), video games and exergames that can improve physiological and cognitive functioning are also emerging as targets for therapeutic and rehabilitative treatments. Previous studies have proposed specific applications involving non-invasive brain stimulation (NIBS) and virtual environments, but to date these have been uni-directional and restricted to specific applications or proprietary hardware. Here, we describe technology integration methods that enable invasive and non-invasive brain stimulation devices to interface with a cross-platform game engine and development platform for creating bi-directional brain-computer interfaces (BCI) and XR-based interventions. Furthermore, we present a highly-modifiable software framework and methods for integrating deep brain stimulation (DBS) in 2D, 3D, virtual and mixed reality applications, as well as extensible applications for BCI integration in wireless systems. The source code and integrated brain stimulation applications are available online at https://github.com/oxfordbioelectronics/brain-stim-game

Pre-whitening and Null Projection as an Artifact Suppression Method for Electrocorticography Stimulation in Bi-Directional Brain Computer Interfaces.

Electrocorticography (ECoG)-based bi-directional (BD) brain-computer interfaces (BCIs) are a forthcoming technology promising to help restore function to those with motor and sensory deficits. A major problem with this paradigm is that the cortical stimulation necessary to elicit artificial sensation creates strong electrical artifacts that can disrupt BCI operation by saturating recording amplifiers or obscuring useful neural signal. Even with state-of-the-art hardware artifact suppression methods, robust signal processing techniques are still required to suppress residual artifacts that are present at the digital back-end. Herein we demonstrate the effectiveness of a pre-whitening and null projection artifact suppression method using ECoG data recorded during a clinical neurostimulation procedure. Our method achieved a maximum artifact suppression of 21.49 dB and significantly increased the number of artifact-free frequencies in the frequency domain. This performance surpasses that of a more traditional independent component analysis methodology, while retaining a reduced complexity and increased computational efficiency.

A Prototype of a Fully-Implantable Charge-Balanced Artificial Sensory Stimulator for Bi-directional Brain-Computer-Interface (BD-BCI).

Bi-directional brain-computer interfaces (BD-BCI) to restore movement and sensation must achieve concurrent operation of recording and decoding of motor commands from the brain and stimulating the brain with somatosensory feedback. Previously we developed and validated a benchtop prototype of a fully implantable BCI system for motor decoding. Here, a prototype artificial sensory stimulator was integrated into the benchtop system to develop a prototype of a fully-implantable BD-BCI. The artificial sensory stimulator incorporates an active charge balancing mechanism based on pulse-width modulation to ensure safe stimulation for chronically interfaced electrodes to prevent damage to brain tissue and electrodes. The feasibility of the BD-BCI system's active charge balancing was tested in phantom brain tissue. With the charge-balancing, the removal of the residual charges on an electrode was evident. This is a critical milestone toward fully-implantable BD-BCI systems.

Chapter 13 - Bidirectional brain-computer interfaces

Brain-computer interfaces (BCIs) are devices that interface with the brain to enable interaction with the environment. BCIs have the potential to improve the quality of life for many individuals affected by debilitating disorders of the brain, spine, limbs, and sensory organs through direct interface with the nervous system. While much progress has been made in terms of BCI motor control, significantly less attention has been given to the restoration of tactile, or cutaneous sensations, which can be very important during grasping or manipulation of objects. BCIs will need to integrate both the motor and sensory modalities to truly restore arm and hand function. Here we describe a bidirectional BCI, a system which translates neural signals recorded from the motor cortex into signals to control a device and provides somatosensory feedback by translating external sensor information to electric stimulation patterns delivered to the cortex. In this chapter, we review the neuroscience of somatosensation, the history of sensory feedback in BCI applications, specifically for restoration of hand function and cutaneous sensations, and describe additional work that needs to be completed to make bidirectional BCI a clinical reality.

Artifact-free recordings in human bidirectional brain–computer interfaces

Objective. Intracortical microstimulation has shown promise as a means of evoking somatosensory percepts as part of a bidirectional brain–computer interface (BCI). However, microstimulation generates large electrical artifacts that dominate the recordings necessary for BCI control. These artifacts must be eliminated from the signal in real-time to allow for uninterrupted BCI decoding. Approach. We present a simple, robust modification to an existing clinical BCI system to allow for simultaneous recording and stimulation using a combination of signal blanking and digital filtering, without needing to explicitly account for varying parameters such as electrode locations or amplitudes. We validated our artifact rejection scheme by recording from microelectrodes in primary motor cortex (M1) while stimulating in somatosensory cortex of a person with a spinal cord injury. Main results. M1 recordings were digitally blanked using a sample-and-hold circuit triggered just prior to stimulus onset and a first-order 750 Hz high-pass Butterworth filter was used to reduce distortion of the remaining artifact. This scheme enabled spike detection in M1 to resume as soon as 740 µs after each stimulus pulse. We demonstrated the effectiveness of the complete bidirectional BCI system by comparing functional performance during a 5 degree of freedom robotic arm control task, with and without stimulation. When stimulation was delivered without this artifact rejection scheme, the number of objects the subject was able to move across a table in 2 min under BCI control declined significantly compared to trials without stimulation (p < 0.01). When artifact rejection was implemented, performance was no different than in trials that did not include stimulation (p = 0.621). Significance. The proposed technique uses simple changes in filtering and digital signal blanking with FDA-cleared hardware and enables artifact-free recordings during bidirectional BCI control.

Characterization of Stimulation Artifact Behavior in Simultaneous Electrocorticography Grid Stimulation and Recording.

Bi-directional brain-computer interfaces (BCIs) require simultaneous stimulation and recording to achieve closed-loop operation. It is therefore important that the interface be able to distinguish between neural signals of interest and stimulation artifacts. Current bi-directional BCIs address this problem by temporally multiplexing stimulation and recording. This approach, however, is suboptimal in many BCI applications. Alternative artifact mitigation methods can be devised by investigating the mechanics of artifact propagation. To characterize stimulation artifact behaviors, we collected and analyzed electrocorticography (ECoG) data from eloquent cortex mapping. Ratcheting and phase-locking of stimulation artifacts were observed, as well as dipole-like properties. Artifacts as large as ±1,100 μV appeared as far as 15-37 mm away from the stimulating channel when stimulating at 10 mA. Analysis also showed that the majority of the artifact power was concentrated at the stimulation pulse train frequency (50 Hz) and its super-harmonics (100, 150, 200 Hz). Lower frequencies (0-32 Hz) experienced minimal artifact contamination. These findings could inform the design of future bi-directional ECoG-based BCIs.

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

Restoring motor function with bidirectional neural interfaces.

Closed-loop brain-computer interfaces have bidirectional connections that allow activity-dependent stimulation of the brain, spinal cord, or muscles. Such bidirectional brain-computer interfaces (BBCI) have three major applications that can be used to restore lost motor function. First, the brain could learn to incorporate a long-term artificial recurrent connection into normal behavior, exploiting the brain's ability to adapt to consistent sensorimotor conditions. The obvious clinical application for restoring motor function is to use an artificial recurrent connection to bridge a lost biological connection. Second, activity-dependent stimulation can generate synaptic plasticity on the cellular level. The corresponding clinical application is to strengthen weakened neural connections, such as occur in stroke. A third application involves delivery of activity-dependent deep brain stimulation at subcortical reward sites, which can operantly reinforce the activity that generates the stimulation. The BBCI paradigm has numerous specific applications, depending on the source of the signals and the stimulated targets.