Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates

Department of Neurobiology and Center for Neuroengineering, Duke University, Durham, NC 27710, U.S.A.
Neural Computation (Impact Factor: 2.21). 09/2011; 23(12):3162-204. DOI: 10.1162/NECO_a_00207
Source: PubMed


Brain-machine interfaces (BMIs) transform the activity of neurons recorded in motor areas of the brain into movements of external actuators. Representation of movements by neuronal populations varies over time, during both voluntary limb movements and movements controlled through BMIs, due to motor learning, neuronal plasticity, and instability in recordings. To ensure accurate BMI performance over long time spans, BMI decoders must adapt to these changes. We propose the Bayesian regression self-training method for updating the parameters of an unscented Kalman filter decoder. This novel paradigm uses the decoder's output to periodically update its neuronal tuning model in a Bayesian linear regression. We use two previously known statistical formulations of Bayesian linear regression: a joint formulation, which allows fast and exact inference, and a factorized formulation, which allows the addition and temporary omission of neurons from updates but requires approximate variational inference. To evaluate these methods, we performed offline reconstructions and closed-loop experiments with rhesus monkeys implanted cortically with microwire electrodes. Offline reconstructions used data recorded in areas M1, S1, PMd, SMA, and PP of three monkeys while they controlled a cursor using a handheld joystick. The Bayesian regression self-training updates significantly improved the accuracy of offline reconstructions compared to the same decoder without updates. We performed 11 sessions of real-time, closed-loop experiments with a monkey implanted in areas M1 and S1. These sessions spanned 29 days. The monkey controlled the cursor using the decoder with and without updates. The updates maintained control accuracy and did not require information about monkey hand movements, assumptions about desired movements, or knowledge of the intended movement goals as training signals. These results indicate that Bayesian regression self-training can maintain BMI control accuracy over long periods, making clinical neuroprosthetics more viable.

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Available from: Mikhail A Lebedev, Oct 01, 2015
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    • "The neuroplasticity, induced by biofeedback, could help the subject adjust brain activity to better adapt to the system control over time [51, 76]. On the other hand, the adaptive decoders need to follow the nonstationary neural activities in order to improve the performance of BMI systems [77, 78]. The coadaptive BMI has later been presented as a novel architecture that goes beyond translational neural interface by merging with above two factors [51, 76, 79, 80]. "
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    ABSTRACT: Successful neurological rehabilitation depends on accurate diagnosis, effective treatment, and quantitative evaluation. Neural coding, a technology for interpretation of functional and structural information of the nervous system, has contributed to the advancements in neuroimaging, brain-machine interface (BMI), and design of training devices for rehabilitation purposes. In this review, we summarized the latest breakthroughs in neuroimaging from microscale to macroscale levels with potential diagnostic applications for rehabilitation. We also reviewed the achievements in electrocorticography (ECoG) coding with both animal models and human beings for BMI design, electromyography (EMG) interpretation for interaction with external robotic systems, and robot-assisted quantitative evaluation on the progress of rehabilitation programs. Future rehabilitation would be more home-based, automatic, and self-served by patients. Further investigations and breakthroughs are mainly needed in aspects of improving the computational efficiency in neuroimaging and multichannel ECoG by selection of localized neuroinformatics, validation of the effectiveness in BMI guided rehabilitation programs, and simplification of the system operation in training devices.
    BioMed Research International 09/2014; 2014. DOI:10.1155/2014/286505 · 2.71 Impact Factor
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    • "In recent years, Brain-Machine Interfaces (BMIs) have been shown to restore movement to people living with paralysis via control of external devices such as computer cursors (Wolpaw and McFarland, 2004; Simeral et al., 2011), robotic arms (Hochberg et al., 2006, 2012; Collinger et al., 2013), or one's own limbs through functional electrode stimulation (FES) (Moritz et al., 2008; Pohlmeyer et al., 2009; Ethier et al., 2012). Studies have shown that the BMI control can be affected by several factors such as the type of neural signals used (Wessberg et al., 2000; Mehring et al., 2003; Andersen et al., 2004; Sanchez et al., 2004), long-term stability of the input signals (Santhanam et al., 2006; Flint et al., 2013), type of training signals used for decoders (Miller and Weber, 2011), type of decoders (linear, non-linear, static, adaptive) (Kim et al., 2006; Shenoy et al., 2006; Bashashati et al., 2007; Li et al., 2011), and cortical plasticity that occurs during BMI use (Sanes and Donoghue, 2000; Birbaumer and Cohen, 2007; Daly and Wolpaw, 2008). Other factors include the type of signal used [local field potentials (LFPs), electrocorticograms (ECoG), single or multiunit activity] and the long-term stability of the signals (Schwartz et al., 2006; Chestek et al., 2011; Prasad et al., 2012). "
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    ABSTRACT: Brain-Machine Interfaces (BMIs) can be used to restore function in people living with paralysis. Current BMIs require extensive calibration that increase the set-up times and external inputs for decoder training that may be difficult to produce in paralyzed individuals. Both these factors have presented challenges in transitioning the technology from research environments to activities of daily living (ADL). For BMIs to be seamlessly used in ADL, these issues should be handled with minimal external input thus reducing the need for a technician/caregiver to calibrate the system. Reinforcement Learning (RL) based BMIs are a good tool to be used when there is no external training signal and can provide an adaptive modality to train BMI decoders. However, RL based BMIs are sensitive to the feedback provided to adapt the BMI. In actor-critic BMIs, this feedback is provided by the critic and the overall system performance is limited by the critic accuracy. In this work, we developed an adaptive BMI that could handle inaccuracies in the critic feedback in an effort to produce more accurate RL based BMIs. We developed a confidence measure, which indicated how appropriate the feedback is for updating the decoding parameters of the actor. The results show that with the new update formulation, the critic accuracy is no longer a limiting factor for the overall performance. We tested and validated the system onthree different data sets: synthetic data generated by an Izhikevich neural spiking model, synthetic data with a Gaussian noise distribution, and data collected from a non-human primate engaged in a reaching task. All results indicated that the system with the critic confidence built in always outperformed the system without the critic confidence. Results of this study suggest the potential application of the technique in developing an autonomous BMI that does not need an external signal for training or extensive calibration.
    Frontiers in Neuroscience 05/2014; 8(8):111. DOI:10.3389/fnins.2014.00111 · 3.66 Impact Factor
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    • "There have been a number of recent efforts to learn improved adaptive decoders specifically tailored for the closed loop setting [9] [10], including an approach relying on stochastic optimal control theory [11]. In other contexts, emphasis has been placed on training users to improve closed-loop control [12]. "
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    ABSTRACT: In a closed-loop brain-computer interface (BCI), adaptive decoders are used to learn parameters suited to decoding the user’s neural response. Feedback to the user provides information which permits the neural tuning to also adapt. We present an approach to model this process of co-adaptation between the encoding model of the neural signal and the decoding algorithm as a multi-agent formulation of the linear quadratic Gaussian (LQG) control problem. In simulation we characterize how decoding performance improves as the neural encoding and adaptive decoder optimize, qualitatively resembling experimentally demonstrated closed-loop improvement. We then propose a novel, modified decoder update rule which is aware of the fact that the encoder is also changing and show it can improve simulated co-adaptation dynamics. Our modeling approach offers promise for gaining insights into co-adaptation as well as improving user learning of BCI control in practical settings.
    Neural Information Processing Systems (NIPS); 12/2013
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