Inference based method for realignment of single trial neuronal responses

Neuronal responses to sensory stimuli or neuronal responses related to behaviour are often extracted by averaging neuronal activity over large number of experimental trials. Such trial-averaging is carried out to reduce noise and to reduce the influence of other signals unrelated to the corresponding stimulus or behaviour. However, if the recorded neuronal responses are jittered in time with respect to the corresponding stimulus or behaviour, averaging over trials may distort the estimation of the underlying neuronal response. Here, we present an algorithm, named dTAV algorithm, for realigning the recorded neuronal activity to an arbitrary internal trigger. Using simulated data, we show that the dTAV algorithm can reduce the jitter of neuronal responses for signal to noise ratios of 0.2 or higher, i.e. in cases where the standard deviation of the noise is up to five times larger than the neuronal response amplitude. By removing the jitter and, therefore, enabling more accurate estimation of neuronal responses, the dTAV algorithm can improve analysis and interpretation of the responses and improve the accuracy of systems relaying on asynchronous detection of events from neuronal recordings.

[1]  S P Levine,et al.  A direct brain interface based on event-related potentials. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[2]  H Spekreijse,et al.  Topography and homogeneity of monkey VI studied through subdurally recorded pattern-evoked potentials , 1989, Visual Neuroscience.

[3]  Eun Jung Hwang,et al.  Brain Control of Movement Execution Onset Using Local Field Potentials in Posterior Parietal Cortex , 2009, The Journal of Neuroscience.

[4]  E. Seidemann,et al.  Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[5]  Andreas Schulze-Bonhage,et al.  Grasp Detection from Human ECoG during Natural Reach-to-Grasp Movements , 2013, PloS one.

[6]  John P. Cunningham,et al.  Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes , 2007, NIPS.

[7]  Stefan Rotter,et al.  Elimination of response latency variability in neuronal spike trains , 2003, Biological Cybernetics.

[8]  Bijan Pesaran,et al.  Neural Correlates of Visual–Spatial Attention in Electrocorticographic Signals in Humans , 2011, Front. Hum. Neurosci..

[9]  Gert Pfurtscheller,et al.  Overt foot movement detection in one single Laplacian EEG derivation , 2008, Journal of Neuroscience Methods.

[10]  Afsheen Afshar,et al.  Free-paced high-performance brain-computer interfaces. , 2007, Journal of neural engineering.

[11]  Stefan Rotter,et al.  Single-trial estimation of neuronal firing rates: From single-neuron spike trains to population activity , 1999, Journal of Neuroscience Methods.

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

[13]  C. Mehring,et al.  Detection of Error Related Neuronal Responses Recorded by Electrocorticography in Humans during Continuous Movements , 2013, PloS one.

[14]  S. Wise,et al.  Neuronal activity preceding directional and nondirectional cues in the premotor cortex of rhesus monkeys. , 1988, Somatosensory & motor research.

[15]  J. Steinier,et al.  Smoothing and differentiation of data by simplified least square procedure. , 1972, Analytical chemistry.

[16]  John Q. Gan,et al.  Unsupervised movement onset detection from EEG recorded during self-paced real hand movement , 2010, Medical & Biological Engineering & Computing.

[17]  B J Richmond,et al.  Temporal encoding of two-dimensional patterns by single units in primate primary visual cortex. II. Information transmission. , 1990, Journal of neurophysiology.

[18]  Luisa Canal,et al.  A normal approximation for the chi-square distribution , 2005, Comput. Stat. Data Anal..

[19]  Roger Salamon,et al.  A statistical method for the estimation of neuronal response latency and its functional interpretation , 1983, Brain Research.

[20]  Andrew M. Clark,et al.  Stimulus onset quenches neural variability: a widespread cortical phenomenon , 2010, Nature Neuroscience.

[21]  Gary E. Birch,et al.  Towards Development of a 3-State Self-Paced Brain-Computer Interface , 2007, Comput. Intell. Neurosci..

[22]  Dennis L Barbour,et al.  Nonuniform High-Gamma (60–500 Hz) Power Changes Dissociate Cognitive Task and Anatomy in Human Cortex , 2011, The Journal of Neuroscience.

[23]  Adrián Ponce-Alvarez,et al.  On the Anticipatory Precue Activity in Motor Cortex , 2012, The Journal of Neuroscience.

[24]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[25]  G. Radons,et al.  Analysis, classification, and coding of multielectrode spike trains with hidden Markov models , 2004, Biological Cybernetics.

[26]  A. Riehle,et al.  Neuronal activity and information processing in motor control: From stages to continuous flow , 1988, Biological Psychology.

[27]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[28]  Byron M. Yu,et al.  Neural Variability in Premotor Cortex Provides a Signature of Motor Preparation , 2006, The Journal of Neuroscience.