Signal properties and stability of a chronically implanted endovascular brain computer interface

Our ability to perform a variety of difficult tasks everything from reasoning about the best chess move, to shooting a free throw, or finely dicing an onion is due to the coordinated activity of populations of neurons throughout the nervoussystem. And yet, we lack an understanding of how the brain generates the activity appropriate for achieving something as simple as pressing an elevator button. In part, this is because we do not know which neural activity patterns the brain is capable of generating, nor how that activity will change with experience. By exploring the structure and constraints on the activity patterns the brain can express, we move closer to understanding how the brain can generate the activity supportive of such a rich variety of behaviors and adaptations. Presently, in studies of arm or eye movements, we typically don't know the causal relationship between neural activity and behavior. Here we use a brain-computer interface (BCI) paradigm to study learning, because the exact relationship between neural activity and behavior is controlled by the experimenter. To generate proficient behavior, the animal must change the activity of the neurons currently being recorded. This provides us with the means to causally relate any observed structure in neural population activity with animals' performance at the task. The focus of this thesis is to characterize the structure and time course of neural population activity during learning. In the first part of this thesis, we note that just as there is more than one way to win a game of chess, the brain has many differentpatterns of neural activity it can produce to drive the same behavior. Which of these redundant options does the brain prefer? We find that the frequency with which animals used different patterns of neural population activity was remarkably similar before and after learning. This suggests that the brain's ability to take advantage of redundancy may be somewhat limited, at least within the span of a few hours. In the second part of this thesis, we asked how internal states such as our arousal, attention, and motivation interact with how we learn new tasks. We identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term \neural engagement. We find that stereotyped changes in neural engagement during learning were unrelated to goal-seeking behavior, but nevertheless influenced how quickly different tasks were learned. Overall, this thesis characterizes a variety of different constraints and influences on how populations of neurons change their activity during learning.

Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodent BMI has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguish between control types at the go cue and target acquisition, respectively. We also found effective connectivity from DLPFC→M1 throughout trials across both control types and Cd→M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

Motor brain-machine interfaces (BMI) allow subjects to control external devices by modulating their neural activity. BMIs record the neural activity, use a mathematical algorithm to estimate the subject's intended movement, actuate an external device, and provide visual feedback of the generated movement to the subject. A critical component of a BMI system is the control algorithm, termed decoder. Significant progress has been made in the design of BMI decoders in recent years resulting in proficient control in non-human primates and humans. In this review article, we discuss the decoding algorithms developed in the BMI field, with particular focus on recent designs that are informed by closed-loop control ideas. A motor BMI can be modeled as a closed-loop control system, where the controller is the brain, the plant is the prosthetic, the feedback is the biofeedback, and the control command is the neural activity. Additionally, compared to other closed-loop systems, BMIs have various unique properties. Neural activity is noisy and stochastic, and often consists of a sequence of spike trains. Neural representations of movement could be non-stationary and change over time, for example as a result of learning. We review recent decoder designs that take these unique properties into account. We also discuss the opportunities that exist at the interface of control theory, statistical inference, and neuroscience to devise a control-theoretic framework for BMI design and help develop the next-generation BMI control algorithms.

One of the efforts on brain-machine interfaces (BMI) is reducing the system delay to perform sophisticated movements. Motor commands in brain are produced in advance before its movement in central nervous system. In the field of electroencephalogram (EEG), this issue has been studied widely as readiness potential (RP) to overcome time critical problem. Neural responses prior to movement in M1, primary motor cortex have been observed. In this paper, we present the neural decoding of finger or wrist movements using neural activity data collected from 0.5 sec. to 1.0 sec. when the movement onset happened at 1.0 sec. With novel temporal window, 30 randomly selected neurons could perform competent decoding accuracy as high as 95%.

<h3>Abstract</h3> <h3>Objective</h3> Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. <h3>Approach</h3> Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. <h3>Main Results</h3> LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. <h3>Significance</h3> These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs. <h3>Suggested PACS</h3> 87.19.L-Neuroscience 87.19.lu motor systems 87.19.rs Movement 87.19.R-Mechanical and electrical properties of tissues and organs 87.85.E-Neural Prosthetics 87.85.Wc Neural engineering 87.85.dd brain-machine interface 87.85.Ng Biological signal processing <h3>IOP Subjects</h3> Medical Physics, Biological Physics

Numerous brain–machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI.

The trend of current brain computer interfaces (BCI) seek to establish bi-directional communication with the brain, for instance, recovering motor functions by externally controlling devices and directly stimulating the brain. This will greatly assist paralyzed individuals through bypassing the damaged brain region. The key process of this communication interface is to decode movements from neural signals and encode information into neural activity. The majority of decoding or pattern classification studies have focused on cortical areas for BCIs, but deep brain structures have also been involved in motor control. The subthalamic nucleus (STN) in the basal ganglia is involved in the preparation, execution and imagining of movements, and may be an alternative source for driving BCIs. This study therefore aimed to classify patterns of deep brain local field potentials (LFPs) related to execution of visually cued movements. LFPs were recorded bilaterally from the STN through deep brain stimulation electrodes implanted in patients with Parkinson's disease. The frequency dependent components of the LFPs were extracted using the wavelet packet transform. In each frequency component, signal features were extracted using an alternative approach called neural synchronization by analyzing Granger causality between the STN. Based on these extracted features, a new feature selection strategy, namely weighted sequential feature selection (WSFS) was developed to efficiently select the optimal feature subset. A support vector machine (SVM) classifier was implemented alongside this novel feature extraction and selection strategy, and evaluated using a cross-validation procedure. Using this optimised feature subset, average correct pattern classification accuracy of movement (left or right) reached 76.0±3.1%. The results obtained in this study are encouraging and suggest that the neural activity in the deep neural circuit (basal ganglia) can be used for controlling BCIs.

Brain-computer interfaces (BCIs)—systems that can record neural activity and translate them into commands for computer systems—are sufficiently advanced to allow users to volitionally guide them through simple tasks. Contemporary BCI research focuses on squeezing additional functionality out of standardized paradigms, be it achieving more bits per second, increased degrees of freedom, or increasing accuracy. While these studies have shown marginal advancements in recent years, our lack of understanding concerning the underlying neurophysiology continues to be the limiting factor in BCI development. In this chapter, we propose turning the way research is done on BCI systems on its head; instead of using our understanding of neural signals to incrementally advance the state of brain-machine interfaces, we apply a BCI system as a form of experimental control to study changes in neural activity. By using current BCI systems as a tool for neuroscientific study, we can probe the underlying neuroanatomy in novel, behaviorally controlled ways.

The upper limb dysfunction resulted from stroke has a high incidence and is hard to recover, seriously affecting the daily life and life quality of patients. The nerve tissue damage caused by stroke usually leads to the injuries of movement, sensory, cognitive and etc. The effective rehabilitation therapy can promote the rebuilding of the surviving cranial nerves to enhance the function and ability. As a modern intelligent nerve rehabilitation technology, brain-machine interface (BCI) directly builds the exchange channel between the brain and the surrounding environment. BCI extracts the brain's neural activity information, and transfers into the command to drive external equipment. BCI does not rely on the peripheral nerve system and muscle tissue. BCI technology is well suited for neurological rehabilitation training, because the repeated training associated with external equipments and BCI feedback can induce a normal exercise mode, indirectly induced the generation of normal brain signal, and promote brain remodeling, so that improve the patient's athletic ability and exercise control level. In this paper, the research progress of BCI technology for the rehabilitation of the upper limb motor function after stroke was reviewed, including the application, the efficacy and the existing limitations. Key words: Stroke; Rehabilitation of the upper limb motor function; Brain-computer interface

The instability of neural recordings can render clinical brain-computer interfaces (BCIs) uncontrollable. Here, we show that the alignment of low-dimensional neural manifolds (low-dimensional spaces that describe specific correlation patterns between neurons) can be used to stabilize neural activity, thereby maintaining BCI performance in the presence of recording instabilities. We evaluated the stabilizer with non-human primates during online cursor control via intracortical BCIs in the presence of severe and abrupt recording instabilities. The stabilized BCIs recovered proficient control under different instability conditions and across multiple days. The stabilizer does not require knowledge of user intent and can outperform supervised recalibration. It stabilized BCIs even when neural activity contained little information about the direction of cursor movement. The stabilizer may be applicable to other neural interfaces and may improve the clinical viability of BCIs.

Brain–machine interface (BMI) provides an alternative route for controlling an external device with one’s intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.

To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects' internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output.

Introduction: Telehealthcare is fundamental to supporting self-management of health and community based care. For people with complex physical disability the technical challenge is often around how to achieve accessible systems alongside limited or deteriorating functional ability. This paper presents user focused research into a brain computer interface (BCI) helping to sustain community based supported living.Aims: To develop a flexible BCI system for use by people with acquired brain injury at home, by providing a range of BCI paradigms to enable functional control over existing home based technologies.Methods: Using qualitative methods of participatory research with a strong lead user focus five participants based at a cedar supported living option have provided data to highlight user needs. In addition, significant user testing within the home environment has validated technical design.Results: User needs and preferences related to functional requirements and preliminary results from testing the BCI (SSVEP) can be presented.Conclusions: Systems for telehealth have become more ubiquitous for people with cognitive impairment. Telecare services for older people are fairly well developed, however, there are a significant number of people with deteriorating physical function who require issues of access to be explored. BCI offers one solution.

Many people with Tourette syndrome are able to volitionally suppress tics, under certain circumstances. To understand better the neural mechanisms that underlie this ability, we used functional magnetic resonance neuroimaging to track regional brain activity during performance of an intentional inhibition task. On some trials, Tourette syndrome and comparison participants internally chose to make or withhold a motor action (a button press), while on other trials, they followed ‘Go’ and ‘NoGo’ instructions to make or withhold the same action. Using representational similarity analysis, a functional magnetic resonance neuroimaging multivariate pattern analysis technique, we assessed how Tourette syndrome and comparison participants differed in neural activity when choosing to make or to withhold an action, relative to externally cued responses on Go and NoGo trials. Analyses were pre-registered, and the data and code are publicly available. We considered similarity of action representations within regions implicated as critical to motor action release or inhibition and to symptom expression in Tourette syndrome, namely the pre-supplementary motor area, inferior frontal gyrus, insula, caudate nucleus and primary motor cortex. Strikingly, in the Tourette syndrome compared to the comparison group, neural activity within the pre-supplementary motor area displayed greater representational similarity across all action types. Within the pre-supplementary motor area, there was lower response-specific differentiation of activity relating to action and inhibition plans and to internally chosen and externally cued actions, implicating the region as a functional nexus in the symptomatology of Tourette syndrome. Correspondingly, patients with Tourette syndrome may experience volitional tic suppression as an effortful and tiring process because, at the top of the putative motor decision hierarchy, activity within the population of neurons facilitating action is overly similar to activity within the population of neurons promoting inhibition. However, not all pre-supplementary motor area group differences survived correction for multiple comparisons. Group differences in representational similarity were also present in the primary motor cortex. Here, representations of internally chosen and externally cued inhibition were more differentiated in the Tourette syndrome group than in the comparison group, potentially a consequence of a weaker voluntary capacity earlier in the motor hierarchy to suppress actions proactively. Tic severity and premonitory sensations correlated with primary motor cortex and caudate nucleus representational similarity, but these effects did not survive correction for multiple comparisons. In summary, more rigid pre-supplementary motor area neural coding across action categories may constitute a central feature of Tourette syndrome, which can account for patients’ experience of ‘unvoluntary’ tics and effortful tic suppression.

The post-stimulation response of neural activities plays an important role to evaluate the effectiveness and safety of neural modulation techniques. Previous studies have established the capability of infrared neural modulation (INM) on neural firing regulation in the central nervous system (CNS); however, the dynamic neural activity after the laser offset has not been well characterized yet. We applied 980-nm infrared diode laser light to irradiate the primary motor cortex of rats, and tungsten electrode was inserted to record the single-unit activity of neurons at the depth of 800-1000μm (layer V of primary motor cortex). The neural activities were assessed through the change of neural firing rate and firing pattern pre- and post-stimulation with various radiant exposures. The results showed that the 980-nm laser could modulate the firing properties of neurons in the deep layer of the cortex. More neurons with post-stimulation response (78% vs. 83%) were observed at higher stimulation intensity (0.803J/cm2 vs. 1.071J/cm2, respectively). The change of firing rate also increased with radiant exposures increasing, and the response lasted up to 4.5s at 1.071J/cm2, which was significantly longer than the theoretical thermal relaxation time. Moreover, the increasing Fano factors indicated the irregularity firing pattern of post-stimulation response. Our results confirmed that neural activity maintained a prolonged post-stimulation response after INM, which may provide necessary measurable data for optimization of INM applications in CNS.