Benchtop and bedside validation of a low-cost programmable cortical stimulator in a testbed for bi-directional brain-computer-interface research

Simulation-Informed Power Budget Estimate of a Fully-Implantable Brain-Computer Interface

This study aims to estimate the maximum power consumption that guarantees a thermally safe operation for a titanium-enclosed chest wall unit (CWU) subcutaneously implanted in the pre-pectoral area. This unit is a central piece of an envisioned fully-implantable bi-directional brain-computer interface (BD-BCI). To this end, we created a thermal simulation model using the finite element method implemented in COMSOL. We also performed a sensitivity analysis to ensure that our predictions were robust against the natural variation of physiological and environmental parameters. Based on this analysis, we predict that the CWU can consume between 378 and 538 mW of power without raising the surrounding tissue's temperature above the thermal safety threshold of 2 ∘ C. This power budget should be sufficient to power all of the CWU's basic functionalities, which include training the decoder, online decoding, wireless data transmission, and cortical stimulation. This power budget assessment provides an important specification for the design of a CWU-an integral part of a fully-implantable BD-BCI system.

Early feasibility of an embedded bi-directional brain-computer interface for ambulation

Current treatments for paraplegia induced by spinal cord injury (SCI) are often limited by the severity of the injury. The accompanying loss of sensory and motor functions often results in reliance on wheelchairs, which in turn causes reduced quality of life and increased risk of co-morbidities. While brain-computer interfaces (BCIs) for ambulation have shown promise in restoring or replacing lower extremity motor functions, none so far have simultaneously implemented sensory feedback functions. Additionally, many existing BCIs for ambulation rely on bulky external hardware that make them ill-suited for non-research set-tings. Here, we present an embedded bi-directional BCI (BDBCI), that restores motor function by enabling neural control over a robotic gait exoskeleton (RGE) and delivers sensory feedback via direct cortical electrical stimulation (DCES) in response to RGE leg swing. A first demonstration with this system was performed with a single subject implanted with electrocorticography electrodes, achieving an average lag-optimized cross-correlation of 0.80±0.08 between cues and decoded states over 5 runs.

Unsupervised Joint Domain Adaptation for Decoding Brain Cognitive States From tfMRI Images

Recent advances in large model and neuroscience have enabled exploration of the mechanism of brain activity by using neuroimaging data. Brain decoding is one of the most promising researches to further understand the human cognitive function. However, current methods excessively depends on high-quality labeled data, which brings enormous expense of collection and annotation of neural images by experts. Besides, the performance of cross-individual decoding suffers from inconsistency in data distribution caused by individual variation and different collection equipments. To address mentioned above issues, a Join Domain Adapative Decoding (JDAD) framework is proposed for unsupervised decoding specific brain cognitive state related to behavioral task. Based on the volumetric feature extraction from task-based functional Magnetic Resonance Imaging (tfMRI) data, a novel objective loss function is designed by the combination of joint distribution regularizer, which aims to restrict the distance of both the conditional and marginal probability distribution of labeled and unlabeled samples. Experimental results on the public Human Connectome Project (HCP) S1200 dataset show that JDAD achieves superior performance than other prevalent methods, especially for fine-grained task with 11.5%-21.6% improvements of decoding accuracy. The learned 3D features are visualized by Grad-CAM to build a combination with brain functional regions, which provides a novel path to learn the function of brain cortex regions related to specific cognitive task in group level.

A Real-Time Non-Implantation Bi-Directional Brain-Computer Interface Solution Without Stimulation Artifacts

The non-implantation bi-directional brain-computer interface (BCI) is a neural interface technology that enables direct two-way communication between the brain and the external world by both "reading" neural signals and "writing" stimulation patterns to the brain. This technology has vast potential applications, such as improving the quality of life for individuals with neurological and mental illnesses and even expanding the boundaries of human capabilities. Nonetheless, non-implantation bi-directional BCIs face challenges in generating real-time feedback and achieving compatibility between stimulation and recording. These issues arise due to the considerable overlap between electrical stimulation frequencies and electrophysiological recording frequencies, as well as the impediment caused by the skull to the interaction of external and internal currents. To address those challenges, this work proposes a novel solution that combines the temporal interference stimulation paradigm and minimally invasive skull modification. A longitudinal animal experiment has preliminarily validated the feasibility of the proposed method. In signal recording experiments, the average impedance of our scheme decreased by 4.59 kΩ , about 67%, compared to the conventional technique at 18 points. The peak-to-peak value of the Somatosensory Evoked Potential increased by 8%. Meanwhile, the signal-to-noise ratio of Steady-State Visual Evoked Potential increased by 5.13 dB, and its classification accuracy increased by 44%. The maximum bandwidth of the resting state rose by 63%. In electrical stimulation experiments, the signal-to-noise ratio of the low-frequency response evoked by our scheme rose by 8.04 dB, and no stimulation artifacts were generated. The experimental results show that signal quality in acquisition has significantly improved, and frequency-band isolation eliminates stimulation artifacts at the source. The acquisition and stimulation pathways are real-time compatible in this non-implantation bi-directional BCI solution, which can provide technical support and theoretical guidance for creating closed-loop adaptive systems coupled with particular application scenarios in the future.

Power Budget of a Skull Unit in a Fully-Implantable Brain-Computer Interface: Bio-Heat Model

The aim of this study is to estimate the maximum power consumption that guarantees the thermal safety of a skull unit (SU). The SU is part of a fully-implantable bi-directional brain computer-interface (BD-BCI) system that aims to restore walking and leg sensation to those with spinal cord injury (SCI). To estimate the SU power budget, we created a bio-heat model using the finite element method (FEM) implemented in COMSOL. To ensure that our predictions were robust against the natural variation of the model's parameters, we also performed a sensitivity analysis. Based on our simulations, we estimated that the SU can nominally consume up to 70 mW of power without raising the surrounding tissues' temperature above the thermal safety threshold of 1°C. When considering the natural variation of the model's parameters, we estimated that the power budget could range between 47 and 81 mW. This power budget should be sufficient to power the basic operations of the SU, including amplification, serialization and A/D conversion of the neural signals, as well as control of cortical stimulation. Determining the power budget is an important specification for the design of the SU and, in turn, the design of a fully-implantable BD-BCI system.