Journal: Journal of neural engineering
We used native sensorimotor representations of fingers in a brain-machine interface (BMI) to achieve immediate online control of individual prosthetic fingers.
Objective. Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. Approach. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Main results. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile patient use, have the potential for wider diagnosis of neurological conditions and will advance brain research.
People who suffer from hearing impairments can find it difficult to follow a conversation in a multi-speaker environment. Current hearing aids can suppress background noise; however, there is little that can be done to help a user attend to a single conversation amongst many without knowing which speaker the user is attending to. Cognitively controlled hearing aids that use auditory attention decoding (AAD) methods are the next step in offering help. Translating the successes in AAD research to real-world applications poses a number of challenges, including the lack of access to the clean sound sources in the environment with which to compare with the neural signals. We propose a novel framework that combines single-channel speech separation algorithms with AAD.
To study electrical stimulation of the lacrimal gland and afferent nerves for enhanced tear secretion, as a potential treatment for dry eye disease. We investigate the response pathways and electrical parameters to safely maximize tear secretion.
Recent research has characterized the anatomical and functional basis of speech perception in the human auditory cortex. These advances have made it possible to decode speech information from activity in brain regions like the superior temporal gyrus, but no published work has demonstrated this ability in real-time, which is necessary for neuroprosthetic brain-computer interfaces.
Objective. Memory accuracy is a major problem in human disease and is the primary factor that defines Alzheimer’s, ageing and dementia resulting from impaired hippocampal function in the medial temporal lobe. Development of a hippocampal memory neuroprosthesis that facilitates normal memory encoding in nonhuman primates (NHPs) could provide the basis for improving memory in human disease states. Approach. NHPs trained to perform a short-term delayed match-to-sample (DMS) memory task were examined with multi-neuron recordings from synaptically connected hippocampal cell fields, CA1 and CA3. Recordings were analyzed utilizing a previously developed nonlinear multi-input multi-output (MIMO) neuroprosthetic model, capable of extracting CA3-to-CA1 spatiotemporal firing patterns during DMS performance. Main results. The MIMO model verified that specific CA3-to-CA1 firing patterns were critical for the successful encoding of sample phase information on more difficult DMS trials. This was validated by the delivery of successful MIMO-derived encoding patterns via electrical stimulation to the same CA1 recording locations during the sample phase which facilitated task performance in the subsequent, delayed match phase, on difficult trials that required more precise encoding of sample information. Significance. These findings provide the first successful application of a neuroprosthesis designed to enhance and/or repair memory encoding in primate brain.
Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective Brain Machine Interfaces (BMIs). These recordings generate enormous amounts of data for transmission & storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: 1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); 2) producing real-time, low-latency, spike sorted data; and 3) long term untethered operation.
Connectome disruption is a hallmark of many neurological diseases and trauma with no current strategies to restore lost long-distance axonal pathways in the brain. We are creating transplantable micro-tissue engineered neural networks (micro-TENNs), which are preformed constructs consisting of embedded neurons and long axonal tracts to integrate with the nervous system to physically reconstitute lost axonal pathways.
Objective. Neural tissue engineering holds incredible potential to restore functional capabilities to damaged neural tissue. It was hypothesized that patterned and functionalized nanofiber scaffolds could control neurite direction and enhance neurite outgrowth. Approach. A method of creating aligned electrospun nanofibers was implemented and fiber characteristics were analyzed using environmental scanning electron microscopy. Nanofibers were composed of polycaprolactone (PCL) polymer, PCL mixed with gelatin, or PCL with a laminin coating. Three-dimensional hydrogels were then integrated with embedded aligned nanofibers to support neuronal cell cultures. Microscopic images were captured at high-resolution in single and multi-focal planes with eGFP-expressing neuronal SH-SY5Y cells in a fluorescent channel and nanofiber scaffolding in another channel. Neuronal morphology and neurite tracking of nanofibers were then analyzed in detail. Main results. Aligned nanofibers were shown to enable significant control over the direction of neurite outgrowth in both two-dimensional (2D) and three-dimensional (3D) neuronal cultures. Laminin-functionalized nanofibers in 3D hyaluronic acid (HA) hydrogels enabled significant alignment of neurites with nanofibers, enabled significant neurite tracking of nanofibers, and significantly increased the distance over which neurites could extend. Specifically, the average length of neurites per cell in 3D HA constructs with laminin-functionalized nanofibers increased by 66% compared to the same laminin fibers on 2D laminin surfaces, increased by 59% compared to 2D laminin-coated surface without fibers, and increased by 1052% compared to HA constructs without fibers. Laminin functionalization of fibers also doubled average neurite length over plain PCL fibers in the same 3D HA constructs. In addition, neurites also demonstrated tracking directly along the fibers, with 66% of neurite lengths directly tracking laminin-coated fibers in 3D HA constructs, which was a 65% relative increase in neurite tracking compared to plain PCL fibers in the same 3D HA constructs and a 213% relative increase over laminin-coated fibers on 2D laminin-coated surfaces. Significance. This work demonstrates the ability to create unique 3D neural tissue constructs using a combined system of hydrogel and nanofiber scaffolding. Importantly, patterned and biofunctionalized nanofiber scaffolds that can control direction and increase length of neurite outgrowth in three-dimensions hold much potential for neural tissue engineering. This approach offers advancements in the development of implantable neural tissue constructs that enable control of neural development and reproduction of neuroanatomical pathways, with the ultimate goal being the achievement of functional neural regeneration.
Direct synthesis of speech from neural signals could provide a fast and natural way of communication to people with neurological diseases. Invasively-measured brain activity (electrocorticography; ECoG) supplies the necessary temporal and spatial resolution to decode fast and complex processes such as speech production. A number of impressive advances in speech decoding using neural signals have been achieved in recent years, but the complex dynamics are still not fully understood. However, it is unlikely that simple linear models can capture the relation between neural activity and continuous spoken speech. Approach. Here we show that deep neural networks can be used to map ECoG from speech production areas onto an intermediate representation of speech (logMel spectrogram). The proposed method uses a densely connected convolutional neural network topology which is well-suited to work with the small amount of data available from each participant. Main results. In a study with six participants, we achieved correlations up to r=0.69 between the reconstructed and original logMel spectrograms. We transfered our prediction back into an audible waveform by applying a Wavenet vocoder. The vocoder was conditioned on logMel features that harnessed a much larger, pre-existing data corpus to provide the most natural acoustic output. Significance. To the best of our knowledge, this is the first time that high-quality speech has been reconstructed from neural recordings during speech production using deep neural networks.