Spiking neural network decoder for brain-machine interfaces

Dethier, Julie; Gilja, Vikash; Nuyujukian, Paul; Elassaad, Shauki; Shenoy, Krishna V.; Boahen, Kwabena

doi:10.1109/ner.2011.5910570

Cited by 13 publications

(12 citation statements)

References 12 publications

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“…Wiener filter, Kalman filter and others: Dethier et al 2011; Acharya et al 2010; Ball et al 2009; Kubanek et al 2009; Liang & Bougrain, 2009; Pistohl et al 2008), the Support Vector Machine (SVM) approach is established as a gold standard for deriving class labels in cases of a discrete set of control commands (Quandt et al 2012; Liu et al 2010; Zhao et al 2010; Demirer et al, 2009; Shenoy et al 2007). This is due to high and reproducible classification performance as well as robustness with a low number of training samples using SVM approaches (Guyon et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography

et al. 2013

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Objective Support Vector Machines (SVM) have developed into a gold standard for accurate classification in Brain-Computer-Interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of Hidden Markov Models (HMM)for online BCIs and discuss strategies to improve their performance. Approach We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from the Electrocorticograms of four subjects doing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. Main results We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. Significance We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online brain-computer interfaces.

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Section: Introductionmentioning

confidence: 99%

Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography

et al. 2013

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“…Recent studies have highlighted the utility of neural networks for decoding in both off-line [37] and online [38] settings. Similarly, we encouragingly achieved off-line (open-loop) SNN performance comparable to that of the traditional floating point implementation [29]. We also realized simulation algorithm enhancements that enable the real-time execution of a 2000-neuron SNN on x86 hardware and reported preliminary closed-loop results obtained with a single monkey performing a single task [39].…”

Section: Cortically-controlled Brain-machine Interfacesmentioning

confidence: 90%

“…Other improvements to the basic SNN consisted of using two one-dimensional integrators instead of a single three-dimensional one, feeding the constant 1 into the two integrators continuously rather than obtaining it internally through integration, and connecting the 192 neural measurements directly to the recurrent pool of neurons, without using the b k ( t ) neurons as an intermediary (figure 5) [29]. …”

Section: Spiking Neural Network Decodermentioning

confidence: 99%

“…We first performed an off-line open-loop validation of our SNN decoder [29] by using a previously recorded BMI experiment that utilized a standard Kalman filter (SKF) with floating point computations. An adult male rhesus macaque (Monkey L) was trained to perform a point-to-point arm movement task in a 3D experimental apparatus for a juice reward [13].…”

Section: Off-line Open-loop Implementationmentioning

confidence: 99%

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Design and validation of a real-time spiking-neural-network decoder for brain–machine interfaces

Dethier¹,

Nuyujukian²,

Ryu³

et al. 2013

J. Neural Eng.

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Objective Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

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“…In this sense no parameters need to be tuned, as they either are calculated using the framework or estimated from neurobiological data. The NEF has been successfully used in modelling a wide variety of neural systems including those involved in sensory processing [11], motor control [20], and cognitive functions [9] such as decision making [32], both matching experimental data (neural and behavioral), and making a variety of novel predictions [33].…”

Section: Neural Engineering Frameworkmentioning

confidence: 99%

Real time on-chip implementation of dynamical systems with spiking neurons

Galluppi

Davies

Furber

et al. 2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Simulation of large-scale networks of spiking neu rons has become appealing for understanding the computational principles of the nervous system by producing models based on biological evidence. In particular, networks that can assume a variety of (dynamically) stable states have been proposed as the basis for different behavioural and cognitive functions. This work focuses on implementing the Neural EngineeringFramework (NEF), a formal method for mapping attractor net works and control-theoretic algorithms to biologically plausible networks of spiking neurons, on the SpiNNaker system, a massive programmable parallel architecture oriented to the simulation of networks of spiking neurons. We describe how to encode and decode analog values to patterns of neural spikes directly on chip. These methods take advantage of the full programmability of the ARM968 cores constituting the processing base of a SpiNNaker node, and exploit the fast Network-on-chip for spike communication.In this paper we focus on the fundamentals of representing, transforming and implementing dynamics in spiking networks.We show real time simulation results demonstrating the NEF principles and discuss advantages, precision and scalability. More generally, the present approach can be used to state and test hypotheses with large-scale spiking neural network models for a range of different cognitive functions and behaviours.

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Spiking neural network decoder for brain-machine interfaces

Cited by 13 publications

References 12 publications

Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography

Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography

Design and validation of a real-time spiking-neural-network decoder for brain–machine interfaces

Real time on-chip implementation of dynamical systems with spiking neurons

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