Multi-Channel Neural Recording Implants: A Review <s> Introduction <s> Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a 'neural cursor' with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis. <s> BIB001 </s> Multi-Channel Neural Recording Implants: A Review <s> Introduction <s> A crucial and important part of a medical diagnostics system is the monitoring of the biopotential signals. This paper describes a complete low-power EEG acquisition ASIC that is suitable for miniaturized ambulatory EEG measurement systems. The aim is not only to improve the patients' comfort but also to extend the device applications. <s> BIB002 </s> Multi-Channel Neural Recording Implants: A Review <s> Introduction <s> Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources--including Na(+) and Ca(2+) spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations--can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal. <s> BIB003 </s> Multi-Channel Neural Recording Implants: A Review <s> Introduction <s> Summary Background Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain–machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface. Methods We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain–machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480. Findings The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0–15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported. Interpretation With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living. Funding Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute. <s> BIB004 </s> Multi-Channel Neural Recording Implants: A Review <s> Introduction <s> Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals. <s> BIB005 </s> Multi-Channel Neural Recording Implants: A Review <s> Introduction <s> Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested. We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement. These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients. <s> BIB006 </s> Multi-Channel Neural Recording Implants: A Review <s> Introduction <s> Cajal's description of the neuron as the structural and functional unit of the nervous system formed the basis of much subsequent neuroscientific research. In this Timeline article, Yuste considers the contribution of the 'neuron doctrine' to neuroscience and discusses the recent transition in paradigm from the single neuron to neural networks. <s> BIB007 </s> Multi-Channel Neural Recording Implants: A Review <s> Introduction <s> New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (electroencephalograms, magnetoencephalograms, electrocorticograms and local field potentials) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide recommendations for interpreting the data using forward and inverse models. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.This article presents best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and highlights issues and limitations of interpretation. <s> BIB008 </s> Multi-Channel Neural Recording Implants: A Review <s> Introduction <s> Technology that translates neural activity into speech would be transformative for people who are unable to communicate as a result of neurological impairments. Decoding speech from neural activity is challenging because speaking requires very precise and rapid multi-dimensional control of vocal tract articulators. Here we designed a neural decoder that explicitly leverages kinematic and sound representations encoded in human cortical activity to synthesize audible speech. Recurrent neural networks first decoded directly recorded cortical activity into representations of articulatory movement, and then transformed these representations into speech acoustics. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. Intermediate articulatory dynamics enhanced performance even with limited data. Decoded articulatory representations were highly conserved across speakers, enabling a component of the decoder to be transferrable across participants. Furthermore, the decoder could synthesize speech when a participant silently mimed sentences. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication. <s> BIB009
In the past decade, researchers have worked on the brain to understand its functions and monitor the brain's electrical signals to research, diagnose and treat its disorders, as well as to utilize these signals to control artificial limbs. Brain-machine interfaces (BMIs) can serve people with different clinical disorders. For example, researchers have implemented robotic limbs  BIB004 , speech synthesizers BIB009 , and human neuroprosthetic control of computer cursors BIB001 BIB005 BIB006 , utilizing less than 300 electrodes BIB007 . In addition, monitoring the bio-potential signals is a fundamental and vital part of a medical diagnostics system. For this purpose, patients are generally connected to a massive bio-potential acquisition equipment. However, this limits the patient's daily routine on the one hand, and on the other hand requires the long-term arduous monitoring of diagnostics BIB002 . One of the most promising solutions is to use neural recording implants as a part of BMI systems, which are in high demand and are being developed and improved as technology develops. Inability to record from large numbers of neurons has limited the development of BMI. Noninvasive methods are capable of recording millions of neurons through the skull, however this signal is nonspecific and distorted BIB003 BIB008 . Utilizing electrodes placed on the surface of the cortex,