Decoding Kinematics from Human Parietal Cortex using Neural Networks

Shah, Sahil; Haghi, Benyamin; Kellis, Spencer; Bashford, Luke; Kramer, Daniel R.; Lee, Brian; Liu, Charles; Andersen, Richard A.; Emami, Azita

doi:10.1109/ner.2019.8717137

Cited by 11 publications

(9 citation statements)

References 13 publications

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“…4) Experiment 4: In this experiment (see Fig. 2 (b)), NHP B was trained to perform the classical centre-out task [13], [26], [27] through joystick control with targets appearing in one of the 8 different locations. A cursor appeared at the centre of the screen at the beginning of every trial and a trial was considered successful if the NHP managed to manipulate the cursor to reach and stay in the target area for 2 seconds under a total elapsed time of 10 seconds.…”

Section: B Behavioural Tasksmentioning

confidence: 99%

Lightweight Reinforcement Algorithms for autonomous, scalable intra-cortical Brain Machine Interfaces

Shaikh

Sibindi

et al. 2020

Preprint

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Intra-cortical Brain Machine Interfaces (iBMIs) with wireless capability could scale the number of recording channels by integrating an intention decoder to reduce data rates. However, the need for frequent retraining due to neural signal non-stationarity is a big impediment. This paper presents an alternate paradigm of online reinforcement learning (RL) with a binary evaluative feedback in iBMIs to tackle this issue. This paradigm eliminates time-consuming calibration procedures. Instead, it relies on updating the model on a sequential sample-by-sample basis based on an instantaneous evaluative binary feedback signal. However, batch updates of weight in popular deep networks is very resource consuming and incompatible with constraints of an implant. In this work, using offline open-loop analysis on pre-recorded data, we show application of a simple RL algorithm - Banditron -in discrete-state iBMIs and compare it against previously reported state of the art RL algorithms – Hebbian RL, Attention gated RL, deep Q-learning. Owing to its simplistic single-layer architecture, Banditron is found to yield at least two orders of magnitude of reduction in power dissipation compared to state of the art RL algorithms. At the same time, post-hoc analysis performed on four pre-recorded experimental datasets procured from the motor cortex of two non-human primates performing joystick-based movement-related tasks indicate Banditron performing significantly better than state of the art RL algorithms by at least 5%, 10%, 7% and 7% in experiments 1, 2, 3 and 4 respectively. Furthermore, we propose a non-linear variant of Banditron, Banditron-RP, which gives an average improvement of 6%, 2% in decoding accuracy in experiments 2,4 respectively with only a moderate increase in power consumption.

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Section: B Behavioural Tasksmentioning

confidence: 99%

Lightweight Reinforcement Algorithms for autonomous, scalable intra-cortical Brain Machine Interfaces

Shaikh

Sibindi

et al. 2020

Preprint

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“…Second, several types of NNs can successfully control motor movement in BMIs. These include recurrent NN (RNN) (Haykin, 1994;Shah et al, 2019), echo-state network (ESN) (Jaeger and Haas, 2004), and time-delay NN (TDNN) (Waibel et al, 1989). RNNs have feedback connections that are capable of processing neural signal sequences.…”

Section: Introductionmentioning

confidence: 99%

“…TDNNs are feedforward NNs with delayed versions of inputs that implement a short-term memory mechanism (Waibel et al, 1989). Of these NNs, RNNs are highly accurate in BMI applications (Sanchez et al, 2004(Sanchez et al, , 2005Sussillo et al, 2012;Kifouche et al, 2014;Shah et al, 2019). Therefore, the present work designed an RNN with error feedback as the neural decoder.…”

Section: Introductionmentioning

confidence: 99%

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

Yang

Wang

et al. 2020

Front. Comput. Neurosci.

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“…However, they all have been applied to motor cortex data by mostly using neural firing rates as input features, which show more variability over long periods [2]. Recent work has demonstrated that neural activity in the posterior parietal cortex (PPC) can be used to support BMIs [12,13,14,15,16,17,18], although the encoding of movement kinematics appears to be complex. PPC processes a rich set of high-level aspects of movement including sensory integration, planning, and execution [13] and may encode this information differently [15].…”

mentioning

confidence: 99%

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

Haghi

Kellis

Shah

et al. 2019

Preprint

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We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single-and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs.Recently, nonlinear machine learning algorithms have shown promise in attaining high performance and robustness in BMIs. For instance, Wessberg et al.[5] apply a fully-connected neural network to neural data recorded from a monkey. Shpigelman et al. [6] show that a Gaussian kernel outperforms a linear kernel in a Kernel Auto-Regressive Moving Average (KARMA) algorithm when decoding 3D kinematics from macaque neural activity. Sussillo et al. [7] apply a large FORCE Dynamic Recurrent Neural Network (F-DRNN) on neural data recorded from the primary motor cortex in two monkeys, and then they test the stability of the model over multiple days [8]. Zhang et al. [9] and Schwemmer et al. [10] extract wavelet based features of motor cortex neural data of a human subject to classify intended hand movements by using a nonlinear support vector machine (SVM) and a large deep neural network, respectively. Hosman et al. [11] pass motor cortex neural firing rates to an LSTM and a Kalman filter to compare their performances for decoding intended cursor velocity of a human subject. These nonlinear learning-based decoders have shown more stability over multiple days and have improved performance compared to prior linear methods. However, they all have been applied to motor cortex data by mostly using neural firing rates as input features, which show more variability over long periods [2]. Recent work has demonstrated that neural activity in the posterior parietal cortex (PPC) can be used to support BMIs [12,13,14,15,16,17,18], although the encoding of movement kinematics appears to be complex. PPC processes a rich set of high-level aspects of movement including sensory integration, planning, an...

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Decoding Kinematics from Human Parietal Cortex using Neural Networks

Cited by 11 publications

References 13 publications

Lightweight Reinforcement Algorithms for autonomous, scalable intra-cortical Brain Machine Interfaces

Lightweight Reinforcement Algorithms for autonomous, scalable intra-cortical Brain Machine Interfaces

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

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