Impact of referencing scheme on decoding performance of LFP-based brain-machine interface

Objective There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs. Approach To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks. Main results Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions. Significance Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.

[1]  C. Schroeder,et al.  How Local Is the Local Field Potential? , 2011, Neuron.

[2]  Marc H Schieber,et al.  State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements. , 2013, Journal of neurophysiology.

[3]  F. Offner,et al.  The EEG as potential mapping: the value of the average monopolar reference. , 1950, Electroencephalography and clinical neurophysiology.

[4]  Arjun K. Bansal,et al.  Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials. , 2012, Journal of neurophysiology.

[5]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[6]  Paul Nuyujukian,et al.  A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes , 2015, bioRxiv.

[7]  Joseph G. Makin,et al.  Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm , 2018, Journal of neural engineering.

[8]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[9]  L. Miller,et al.  Accurate decoding of reaching movements from field potentials in the absence of spikes , 2012, Journal of neural engineering.

[10]  M. Carandini,et al.  Local Origin of Field Potentials in Visual Cortex , 2009, Neuron.

[11]  J. Fermaglich.  Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[12]  J. Maunsell,et al.  Different Origins of Gamma Rhythm and High-Gamma Activity in Macaque Visual Cortex , 2011, PLoS biology.

[13]  Chris Eliasmith,et al.  Hyperopt: a Python library for model selection and hyperparameter optimization , 2015 .

[14]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[15]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[16]  Shaomin Zhang,et al.  Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex , 2014, Journal of neural engineering.

[17]  Anish A. Sarma,et al.  Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals. , 2018, Journal of neurophysiology.

[18]  Sonja Grün,et al.  Local field potentials in primate motor cortex encode grasp kinetic parameters , 2015, NeuroImage.

[19]  Shaomin Zhang,et al.  Long-term decoding stability of local field potentials from silicon arrays in primate motor cortex during a 2D center out task , 2014, Journal of neural engineering.

[20]  Mingzhou Ding,et al.  Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations , 2016, Front. Syst. Neurosci..

[21]  Francis R. Willett,et al.  High performance communication by people with paralysis using an intracortical brain-computer interface , 2017, eLife.

[22]  Dror Cohen,et al.  The Effect of Common Signals on Power, Coherence and Granger Causality: Theoretical Review, Simulations, and Empirical Analysis of Fruit Fly LFPs Data , 2018, Front. Syst. Neurosci..

[23]  Robert D Flint,et al.  Long term, stable brain machine interface performance using local field potentials and multiunit spikes , 2013, Journal of neural engineering.

[24]  Shih-Chieh Lin,et al.  Unmasking local activity within local field potentials (LFPs) by removing distal electrical signals using independent component analysis , 2016, NeuroImage.

[25]  John P. Donoghue,et al.  Decoding 3-D Reach and Grasp Kinematics From High-Frequency Local Field Potentials in Primate Primary Motor Cortex , 2010, IEEE Transactions on Biomedical Engineering.

[26]  Dennis J McFarland,et al.  The advantages of the surface Laplacian in brain-computer interface research. , 2015, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[27]  R. Shapley,et al.  Spatial Spread of the Local Field Potential and its Laminar Variation in Visual Cortex , 2009, The Journal of Neuroscience.

[28]  Kip A Ludwig,et al.  Using a common average reference to improve cortical neuron recordings from microelectrode arrays. , 2009, Journal of neurophysiology.

[29]  Franklin D. Johnston,et al.  Electrocardiograms that represent the potential variations of a single electrode , 1934 .

[30]  Alexander S. Ecker,et al.  Feature Selectivity of the Gamma-Band of the Local Field Potential in Primate Primary Visual Cortex , 2008, Front. Neurosci..

[31]  Girijesh Prasad,et al.  Current Source Density Estimation Enhances the Performance of Motor-Imagery-Related Brain–Computer Interface , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[33]  Timothy G. Constandinou,et al.  Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network , 2019, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER).

[34]  C. Mehring,et al.  Inference of hand movements from local field potentials in monkey motor cortex , 2003, Nature Neuroscience.

[35]  Anish A. Sarma,et al.  Clinical translation of a high-performance neural prosthesis , 2015, Nature Medicine.

[36]  Arthur Gretton,et al.  Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information , 2008, The Journal of Neuroscience.

[37]  Supratim Ray,et al.  Effect of Reference Scheme on Power and Phase of the Local Field Potential , 2016, Neural Computation.

[38]  B. Hjorth.  An on-line transformation of EEG scalp potentials into orthogonal source derivations. , 1975, Electroencephalography and clinical neurophysiology.

[39]  Stavros Zanos,et al.  Relationships between spike-free local field potentials and spike timing in human temporal cortex. , 2012, Journal of neurophysiology.

[40]  J. Wolpaw,et al.  Decoding two-dimensional movement trajectories using electrocorticographic signals in humans , 2007, Journal of neural engineering.

[41]  Agrita Dubey,et al.  Spatial spread of local field potential is band-pass in the primary visual cortex , 2016, Journal of neurophysiology.

[42]  Gustavo Deco,et al.  Discrepancies between Multi-Electrode LFP and CSD Phase-Patterns: A Forward Modeling Study , 2016, Front. Neural Circuits.

[43]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[44]  John P. Donoghue,et al.  Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution , 2015, Front. Syst. Neurosci..

[45]  J. W. Osselton.  Bipolar, Unipolar and Average Reference Recording Methods I: Mainly Theoretical Considerations , 1966 .

[46]  Anish A. Sarma,et al.  Volitional control of single-electrode high gamma local field potentials by people with paralysis. , 2019, Journal of neurophysiology.

[47]  R. Andersen,et al.  Selecting the signals for a brain–machine interface , 2004, Current Opinion in Neurobiology.