Brain-machine Interfaces Decoder Research Articles

Unsupervised Neural Manifold Alignment for Stable Decoding of Movement from Cortical Signals.

The stable decoding of movement parameters using neural activity is crucial for the success of brain-machine interfaces (BMIs). However, neural activity can be unstable over time, leading to changes in the parameters used for decoding movement, which can hinder accurate movement decoding. To tackle this issue, one approach is to transfer neural activity to a stable, low-dimensional manifold using dimensionality reduction techniques and align manifolds across sessions by maximizing correlations of the manifolds. However, the practical use of manifold stabilization techniques requires knowledge of the true subject intentions such as target direction or behavioral state. To overcome this limitation, an automatic unsupervised algorithm is proposed that determines movement target intention before manifold alignment in the presence of manifold rotation and scaling across sessions. This unsupervised algorithm is combined with a dimensionality reduction and alignment method to overcome decoder instabilities. The effectiveness of the BMI stabilizer method is represented by decoding the two-dimensional (2D) hand velocity of two rhesus macaque monkeys during a center-out-reaching movement task. The performance of the proposed method is evaluated using correlation coefficient and R-squared measures, demonstrating higher decoding performance compared to a state-of-the-art unsupervised BMI stabilizer. The results offer benefits for the automatic determination of movement intents in long-term BMI decoding. Overall, the proposed method offers a promising automatic solution for achieving stable and accurate movement decoding in BMI applications.

Selective modulation of cortical population dynamics during neuroprosthetic skill learning

Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.

Extracting synchronized neuronal activity from local field potentials based on a marked point process framework

Objective. Brain-machine interfaces (BMIs) translate neural activity into motor commands to restore motor functions for people with paralysis. Local field potentials (LFPs) are promising for long-term BMIs, since the quality of the recording lasts longer than single neuronal spikes. Inferring neuronal spike activity from population activities such as LFPs is challenging, because LFPs stem from synaptic currents flowing in the neural tissue produced by various neuronal ensembles and reflect neural synchronization. Existing studies that combine LFPs with spikes leverage the spectrogram of the former, which can neither detect the transient characteristics of LFP features (here, neuromodulation in a specific frequency band) with high accuracy, nor correlate them with relevant neuronal activity with a sufficient time resolution. Approach. We propose a feature extraction and validation framework to directly extract LFP neuromodulations related to synchronized spike activity using recordings from the primary motor cortex of six Sprague Dawley rats during a lever-press task. We first select important LFP frequency bands relevant to behavior, and then implement a marked point process (MPP) methodology to extract transient LFP neuromodulations. We validate the LFP feature extraction by examining the correlation with the pairwise synchronized firing probability of important neurons, which are selected according to their contribution to behavioral decoding. The highly correlated synchronized firings identified by the LFP neuromodulations are fed into a decoder to check whether they can serve as a reliable neural data source for movement decoding. Main results. We find that the gamma band (30–80 Hz) LFP neuromodulations demonstrate significant correlation with synchronized firings. Compared with traditional spectrogram-based method, the higher-temporal resolution MPP method captures the synchronized firing patterns with fewer false alarms, and demonstrates significantly higher correlation than single neuron spikes. The decoding performance using the synchronized neuronal firings identified by the LFP neuromodulations can reach 90% compared to the full recorded neuronal ensembles. Significance. Our proposed framework successfully extracts the sparse LFP neuromodulations that can identify temporal synchronized neuronal spikes with high correlation. The identified neuronal spike pattern demonstrates high decoding performance, which suggest LFP can be used as an effective modality for long-term BMI decoding.

Spike prediction on primary motor cortex from medial prefrontal cortex during task learning

Objectives. Brain–machine interfaces (BMIs) aim to help people with motor disabilities by interpreting brain signals into motor intentions using advanced signal processing methods. Currently, BMI users require intensive training to perform a pre-defined task, not to mention learning a new task. Thus, it is essential to understand neural information pathways among the cortical areas in task learning to provide principles for designing BMIs with learning abilities. We propose to investigate the relationship between the medial prefrontal cortex (mPFC) and primary motor cortex (M1), which are actively involved in motor control and task learning, and show how information is conveyed in spikes between the two regions on a single-trial basis by computational models. Approach. We are interested in modeling the functional relationship between mPFC and M1 activities during task learning. Six Sprague Dawley rats were trained to learn a new behavioral task. Neural spike data was recorded from mPFC and M1 during learning. We then implement the generalized linear model, the second-order generalized Laguerre–Volterra model, and the staged point-process model to predict M1 spikes from mPFC spikes across multiple days during task learning. The prediction performance is compared across different models or learning stages to reveal the relationship between mPFC and M1 spike activities. Main results. We find that M1 neural spikes can be well predicted from mPFC spikes on the single-trial level, which indicates a highly correlated relationship between mPFC and M1 activities during task learning. By comparing the performance across models, we find that models with higher nonlinear capacity perform significantly better than linear models. This indicates that predicting M1 activity from mPFC activity requires the model to consider higher-order nonlinear interactions beyond pairwise interactions. We also find that the correlation coefficient between the mPFC and M1 spikes increases during task learning. The spike prediction models perform the best when the subjects become well trained on the new task compared with the early and middle stages. The results suggest that the co-activation between mPFC and M1 activities evolves during task learning, and becomes stronger as subjects become well trained. Significance. This study demonstrates that the dynamic patterns of M1 spikes can be predicted from mPFC spikes during task learning, and this will further help in the design of adaptive BMI decoders for task learning.

Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network.

Implanted microelectrode arrays can directly pick up electrode signals from the primary motor cortex (M1) during movement, and brain-machine interfaces (BMIs) can decode these signals to predict the directions of contemporaneous movements. However, it is not well known how much each individual input is responsible for the overall performance of a BMI decoder. In this paper, we seek to quantify how much each channel contributes to an artificial neural network (ANN)-based decoder, by measuring how much the removal of each individual channel degrades the accuracy of the output. If information on movement direction was equally distributed among channels, then the removal of one would have a minimal effect on decoder accuracy. On the other hand, if that information was distributed sparsely, then the removal of specific information-rich channels would significantly lower decoder accuracy. We found that for most channels, their removal did not significantly affect decoder performance. However, for a subset of channels (16 out of 61), removing them significantly reduced the decoder accuracy. This suggests that information is not uniformly distributed among the recording channels. We propose examining these channels further to optimize BMIs more effectively, as well as understand how M1 functions at the neuronal level.

Changes in Modulation Characteristics of Neurons in Different Modes of Motion Control Using Brain-Machine Interface.

In the research of motion control using brain-machine interface (BMI), analysis is usually conducted on one ensemble of neurons whose activity serves as direct input to the BMI decoder (control units). The number of control units is diverse in different control modes. That is to say, the size of dimensions of neural signals used in motion control is diverse. However, how will the behavioral performance change with this kind of diversity? What effects does this diversity have on modulation characteristics of control units? To answer these questions, we designed three modes of motion tasks using neural signals with different dimension sizes to control. Our results imply that as the dimension reduces, some deviations appear in behavioral performance. At the same time, the control units tend to have a directional division of control, then enhance their stability and increase modulations after division.

Cortical Control of Virtual Self-Motion Using Task-Specific Subspaces.

Brain-machine interfaces (BMIs) for reaching have enjoyed continued performance improvements, yet there remains significant need for BMIs that control other movement classes. Recent scientific findings suggest that the intrinsic covariance structure of neural activity depends strongly on movement class, potentially necessitating different decode algorithms across classes. To address this possibility, we developed a self-motion BMI based on cortical activity as monkeys cycled a hand-held pedal to progress along a virtual track. Unlike during reaching, we found no high-variance dimensions that directly correlated with to-be-decoded variables. This was due to no neurons having consistent correlations between their responses and kinematic variables. Yet we could decode a single variable-self-motion-by nonlinearly leveraging structure that spanned multiple high-variance neural dimensions. Resulting online BMI-control success rates approached those during manual control. These findings make two broad points regarding how to build decode algorithms that harmonize with the empirical structure of neural activity in motor cortex. First, even when decoding from the same cortical region (e.g., arm-related motor cortex), different movement classes may need to employ very different strategies. Although correlations between neural activity and hand velocity are prominent during reaching tasks, they are not a fundamental property of motor cortex and cannot be counted on to be present in general. Second, although one generally desires a low-dimensional readout, it can be beneficial to leverage a multidimensional high-variance subspace. Fully embracing this approach requires highly nonlinear approaches tailored to the task at hand, but can produce near-native levels of performance.SIGNIFICANCE STATEMENT Many brain-machine interface decoders have been constructed for controlling movements normally performed with the arm. Yet it is unclear how these will function beyond the reach-like scenarios where they were developed. Existing decoders implicitly assume that neural covariance structure, and correlations with to-be-decoded kinematic variables, will be largely preserved across tasks. We find that the correlation between neural activity and hand kinematics, a feature typically exploited when decoding reach-like movements, is essentially absent during another task performed with the arm: cycling through a virtual environment. Nevertheless, the use of a different strategy, one focused on leveraging the highest-variance neural signals, supported high performance real-time brain-machine interface control.

Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface

Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description—wavelet average coefficients (WAC)—to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders ( Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.

Hierarchical Dynamical Model for Multiple Cortical Neural Decoding.

Motor brain machine interfaces (BMIs) interpret neural activities from motor-related cortical areas in the brain into movement commands to control a prosthesis. As the subject adapts to control the neural prosthesis, the medial prefrontal cortex (mPFC), upstream of the primary motor cortex (M1), is heavily involved in reward-guided motor learning. Thus, considering mPFC and M1 functionality within a hierarchical structure could potentially improve the effectiveness of BMI decoding while subjects are learning. The commonly used Kalman decoding method with only one simple state model may not be able to represent the multiple brain states that evolve over time as well as along the neural pathway. In addition, the performance of Kalman decoders degenerates in heavy-tailed nongaussian noise, which is usually generated due to the nonlinear neural system or influences of movement-related noise in online neural recording. In this letter, we propose a hierarchical model to represent the brain states from multiple cortical areas that evolve along the neural pathway. We then introduce correntropy theory into the hierarchical structure to address the heavy-tailed noise existing in neural recordings. We test the proposed algorithm on in vivo recordings collected from the mPFC and M1 of two rats when the subjects were learning to perform a lever-pressing task. Compared with the classic Kalman filter, our results demonstrate better movement decoding performance due to the hierarchical structure that integrates the past failed trial information over multisite recording and the combination with correntropy criterion to deal with noisy heavy-tailed neural recordings.

Feature-Selection-Based Transfer Learning for Intracortical Brain–Machine Interface Decoding

The time spent in collecting current samples for decoder calibration and the computational burden brought by high-dimensional neural recordings remain two challenging problems in intracortical brain-machine interfaces (iBMIs). Decoder calibration optimization approaches have been proposed, and neuron selection methods have been used to reduce computational burden. However, few methods can solve both problems simultaneously. In this article, we present a symmetrical-uncertainty-based transfer learning (SUTL) method that combines transfer learning with feature selection. The proposed method uses symmetrical uncertainty to quantitatively measure three indices for feature selection: stationarity, importance and redundancy of the feature. By selecting the stationary features, the disparities between the historical data and current data can be diminished, and the historical data can be effectively used for decoder calibration, thereby reducing the demand for current data. After selecting the important and non-redundant features, only the channels corresponding to them need to work; thus, the computational burden is reduced. The proposed method was tested on neural data recorded from two rhesus macaques to decode the reaching position or grasping gesture. The results showed that the SUTL method diminished the disparities between the historical data and current data, while achieving superior decoding performance with the needs of only ten current samples each category, less than 10% the number of features and 30% the number of neural recording channels. Additionally, unlike most studies on iBMIs, feature selection was implemented instead of neuron selection, and the average decoding accuracy achieved by the former was 6.6% higher.

User Adaptation to Closed-Loop Decoding of Motor Imagery Termination.

Here, we provide insights in the decoding of motor imagery termination and investigate the use of such decoder in closed-loop BMI. Participants (N = 9) were asked to perform kinesthetic motor imagery of both hands simultaneously cued with a clock indicating the initiation and termination of the action. Using electroencephalogram (EEG) signals, we built a decoder to detect the transition between event-related desynchronization and event-related synchronization. Features for this decoder were correlates of motor termination in the upper μ and β bands. The decoder reached an accuracy of 76.2% (N = 9), revealing the high robustness of our approach. More importantly, this paper shows that the decoding of motor termination has an intrinsic latency mainly due to the delayed appearance of its correlates. Because the latency was consistent and thus predictable, users were able to compensate it after training. Using our decoding system, BMI users were able to adapt their behavior and modulate their sensorimotor rhythm to stop the device (clock) accurately on time. These results show the importance of closed-loop evaluations of BMI decoders and open new possibilities for BMI control using decoding of movement termination.

Task Learning Over Multi-Day Recording via Internally Rewarded Reinforcement Learning Based Brain Machine Interfaces.

Autonomous brain machine interfaces (BMIs) aim to enable paralyzed people to self-evaluate their movement intention to control external devices. Previous reinforcement learning (RL)-based decoders interpret the mapping between neural activity and movements using the external reward for well-trained subjects, and have not investigated the task learning procedure. The brain has developed a learning mechanism to identify the correct actions that lead to rewards in the new task. This internal guidance can be utilized to replace the external reference to advance BMIs as an autonomous system. In this study, we propose to build an internally rewarded reinforcement learning-based BMI framework using the multi-site recording to demonstrate the autonomous learning ability of the BMI decoder on the new task. We test the model on the neural data collected over multiple days while the rats were learning a new lever discrimination task. The primary motor cortex (M1) and medial prefrontal cortex (mPFC) spikes are interpreted by the proposed RL framework into the discrete lever press actions. The neural activity of the mPFC post the action duration is interpreted as the internal reward information, where a support vector machine is implemented to classify the reward vs. non-reward trials with a high accuracy of 87.5% across subjects. This internal reward is used to replace the external water reward to update the decoder, which is able to adapt to the nonstationary neural activity during subject learning. The multi-cortical recording allows us to take in more cortical recordings as input and uses internal critics to guide the decoder learning. Comparing with the classic decoder using M1 activity as the only input and external guidance, the proposed system with multi-cortical recordings shows a better decoding accuracy. More importantly, our internally rewarded decoder demonstrates the autonomous learning ability on the new task as the decoder successfully addresses the time-variant neural patterns while subjects are learning, and works asymptotically as the subjects' behavioral learning progresses. It reveals the potential of endowing BMIs with autonomous task learning ability in the RL framework.

Deep Learning Neural Encoders for Motor Cortex.

Intracortical brain-machine interfaces (BMIs) transform neural activity into control signals to drive a prosthesis or communication device, such as a robotic arm or computer cursor. To be clinically viable, BMI decoders must achieve high accuracy and robustness. Optimizing these decoders is expensive, traditionally requiring animal or human experiments spanning months to years. This is because BMIs are closed-loop systems, where the user updates his or her motor commands in response to an imperfectly decoded output. Decoder optimization using previously collected "offline" data will therefore not capture this closed-loop response. An alternative approach to significantly accelerate decoder optimization is to use a closed-loop experimental simulator. A key component of this simulator is the neural encoder, which synthetically generates neural population activity from kinematics. Prior neural encoders do not model important features of neural population activity. To overcome these limitations, we use deep learning neural encoders. We find these models significantly outperform prior neural encoders in reproducing peri-stimulus time histograms (PSTHs) and neural population dynamics. We also find that deep learning neural encoders better match neural decoding results in offline data and closed-loop experimental data. We anticipate these deep-learning neural encoders will substantially improve simulators for BMIs, enabling faster evaluation, optimization, and characterization of BMI decoder algorithms.

Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network.

Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunities for improving the design of BMI decoders, including the use of recurrent artificial neural networks to decode neuronal ensemble activity in real time. Here, we developed a long-short term memory (LSTM) decoder for extracting movement kinematics from the activity of large (N = 134-402) populations of neurons, sampled simultaneously from multiple cortical areas, in rhesus monkeys performing motor tasks. Recorded regions included primary motor, dorsal premotor, supplementary motor, and primary somatosensory cortical areas. The LSTM's capacity to retain information for extended periods of time enabled accurate decoding for tasks that required both movements and periods of immobility. Our LSTM algorithm significantly outperformed the state-of-the-art unscented Kalman filter when applied to three tasks: center-out arm reaching, bimanual reaching, and bipedal walking on a treadmill. Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics across task epochs. LSTM modeled several key physiological attributes of cortical circuits involved in motor tasks. These findings suggest that LSTM-based approaches could yield a better algorithm strategy for neuroprostheses that employ BMIs to restore movement in severely disabled patients.

Paradigm Shift in Sensorimotor Control Research and Brain Machine Interface Control: The Influence of Context on Sensorimotor Representations.

Neural activity in the primary motor cortex (M1) is known to correlate with movement related variables including kinematics and dynamics. Our recent work, which we believe is part of a paradigm shift in sensorimotor research, has shown that in addition to these movement related variables, activity in M1 and the primary somatosensory cortex (S1) are also modulated by context, such as value, during both active movement and movement observation. Here we expand on the investigation of reward modulation in M1, showing that reward level changes the neural tuning function of M1 units to both kinematic as well as dynamic related variables. In addition, we show that this reward-modulated activity is present during brain machine interface (BMI) control. We suggest that by taking into account these context dependencies of M1 modulation, we can produce more robust BMIs. Toward this goal, we demonstrate that we can classify reward expectation from M1 on a movement-by-movement basis under BMI control and use this to gate multiple linear BMI decoders toward improved offline performance. These findings demonstrate that it is possible and meaningful to design a more accurate BMI decoder that takes reward and context into consideration. Our next step in this development will be to incorporate this gating system, or a continuous variant of it, into online BMI performance.

Near Perfect Neural Critic from Motor Cortical Activity Toward an Autonomously Updating Brain Machine Interface.

We are developing an autonomously updating brain machine interface (BMI) utilizing reinforcement learning principles. One aspect of this system is a neural critic that determines reward expectations from neural activity. This critic is then used to update a BMI decoder toward an improved performance from the user's perspective. Here we demonstrate the ability of a neural critic to classify trial reward value given activity from the primary motor cortex (M1), using neural features from single/multi units (SU/MU), and local field potentials (LFPs) with prediction accuracies up to 97% correct. A nonhuman primate subject conducted a cued center out reaching task, either manually, or observationally. The cue indicated the reward value of a trial. Features such as power spectral density (PSD) of the LFPs and spike-field coherence (SFC) between SU/MU and corresponding LFPs were calculated and used as inputs to several classifiers. We conclude that hybrid features of PSD and SFC show higher classification performance than PSD or SFC alone (accuracy was 92% for manual tasks, and 97% for observational). In the future, we will employ these hybrid features toward our autonomously updating BMI.

Augmenting intracortical brain-machine interface with neurally driven error detectors

Objective. Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain–machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. Approach. We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. Main results. We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error ‘detect-and-act’ system that attempts to automatically ‘undo’ or ‘prevent’ mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). Significance. Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.

Influence of artifacts on movement intention decoding from EEG activity in severely paralyzed stroke patients.

Brain-machine interfaces (BMI) can be used to control robotic and prosthetic devices for rehabilitation of motor disorders, such as stroke. The calibration of these BMI systems is of paramount importance in order to establish a precise contingent link between the brain activity related to movement intention and the peripheral feedback. However, electroencephalographic (EEG) activity, commonly used to build non-invasive BMIs, can be easily contaminated by artifacts of electrical or physiological origin. The way these interferences can affect the performance of movement intention decoders has not been deeply studied, especially when dealing with severely paralyzed patients, which often generate more artifacts by compensatory movements. This paper evaluates the effects of removing artifacts from the data used to train a BMI decoder on a dataset of 28 severely paralyzed stroke patients. We show that cleaning the training datasets reduces the global BMI performance for decoding attempts of movement. Further, we demonstrate that this performance drop especially affects the test trials contaminated by artifacts (i.e., trials that might not reflect cortical activity but noise), but not the clean test trials (i.e., trials representing correct cortical activity). This paper underlines the importance of cleaning the datasets used to train BMI systems to improve their efficacy for decoding movement intention and maximize their neurorehabilitative potential.

Feedback for reinforcement learning based brain–machine interfaces using confidence metrics

Objective. For brain–machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-term goal is to create autonomous BMIs that can utilize an evaluative feedback from the brain to update the decoding algorithm and use it intelligently in order to adapt the decoder. In this study, we show how to extract the necessary evaluative feedback from a biologically realistic (synthetic) source, use both the quantity and the quality of the feedback, and how that feedback information can be incorporated into a reinforcement learning (RL) controller architecture to maximize its performance. Approach. Motivated by the perception–action–reward cycle (PARC) in the brain which links reward for cognitive decision making and goal-directed behavior, we used a reward-based RL architecture named Actor–Critic RL as the model. Instead of using an error signal towards building an autonomous BMI, we envision to use a reward signal from the nucleus accumbens (NAcc) which plays a key role in the linking of reward to motor behaviors. To deal with the complexity and non-stationarity of biological reward signals, we used a confidence metric which was used to indicate the degree of feedback accuracy. This confidence was added to the Actor’s weight update equation in the RL controller architecture. If the confidence was high (>0.2), the BMI decoder used this feedback to update its parameters. However, when the confidence was low, the BMI decoder ignored the feedback and did not update its parameters. The range between high confidence and low confidence was termed as the ‘ambiguous’ region. When the feedback was within this region, the BMI decoder updated its weight at a lower rate than when fully confident, which was decided by the confidence. We used two biologically realistic models to generate synthetic data for MI (Izhikevich model) and NAcc (Humphries model) to validate proposed controller architecture. Main results. In this work, we show how the overall performance of the BMI was improved by using a threshold close to the decision boundary to reject erroneous feedback. Additionally, we show the stability of the system improved when the feedback was used with a threshold. Significance: The result of this study is a step towards making BMIs autonomous. While our method is not fully autonomous, the results demonstrate that extensive training times necessary at the beginning of each BMI session can be significantly decreased. In our approach, decoder training time was only limited to 10 trials in the first BMI session. Subsequent sessions used previous session weights to initialize the decoder. We also present a method where the use of a threshold can be applied to any decoder with a feedback signal that is less than perfect so that erroneous feedback can be avoided and the stability of the system can be increased.

Brain-Machine Interface Control Algorithms.

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.