Spatial co-adaptation of cortical control columns in a micro-ECoG brain–computer interface

OBJECTIVE Electrocorticography (ECoG) has been used for a range of applications including electrophysiological mapping, epilepsy monitoring, and more recently as a recording modality for brain-computer interfaces (BCIs). Studies that examine ECoG electrodes designed and implanted chronically solely for BCI applications remain limited. The present study explored how two key factors influence chronic, closed-loop ECoG BCI: (i) the effect of inter-electrode distance on BCI performance and (ii) the differences in neural adaptation and performance when fixed versus adaptive BCI decoding weights are used. APPROACH The amplitudes of epidural micro-ECoG signals between 75 and 105 Hz with 300 μm diameter electrodes were used for one-dimensional and two-dimensional BCI tasks. The effect of inter-electrode distance on BCI control was tested between 3 and 15 mm. Additionally, the performance and cortical modulation differences between constant, fixed decoding using a small subset of channels versus adaptive decoding weights using the entire array were explored. MAIN RESULTS Successful BCI control was possible with two electrodes separated by 9 and 15 mm. Performance decreased and the signals became more correlated when the electrodes were only 3 mm apart. BCI performance in a 2D BCI task improved significantly when using adaptive decoding weights (80%-90%) compared to using constant, fixed weights (50%-60%). Additionally, modulation increased for channels previously unavailable for BCI control under the fixed decoding scheme upon switching to the adaptive, all-channel scheme. SIGNIFICANCE Our results clearly show that neural activity under a BCI recording electrode (which we define as a 'cortical control column') readily adapts to generate an appropriate control signal. These results show that the practical minimal spatial resolution of these control columns with micro-ECoG BCI is likely on the order of 3 mm. Additionally, they show that the combination and interaction between neural adaptation and machine learning are critical to optimizing ECoG BCI performance.

[1]  B. Gordon,et al.  Induced electrocorticographic gamma activity during auditory perception , 2001, Clinical Neurophysiology.

[2]  D. Moran,et al.  Cortical Adaptation to a Chronic Micro-Electrocorticographic Brain Computer Interface , 2013, The Journal of Neuroscience.

[3]  Carla Teixeira Lopes,et al.  TIMIT Acoustic-Phonetic Continuous Speech Corpus , 2012 .

[4]  W. A. Sarnacki,et al.  Electroencephalographic (EEG) control of three-dimensional movement , 2010, Journal of neural engineering.

[5]  Rajesh P. N. Rao,et al.  Localization and classification of phonemes using high spatial resolution electrocorticography (ECoG) grids , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Sonja Grün,et al.  How local is the local field potential? , 2011, BMC Neuroscience.

[7]  G. Buzsáki,et al.  NeuroGrid: recording action potentials from the surface of the brain , 2014, Nature Neuroscience.

[8]  S. P. Levine,et al.  Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement , 2003, Clinical Neurophysiology.

[9]  Jennifer L. Collinger,et al.  The impact of electrode characteristics on electrocorticography (ECoG) , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  W. Freeman,et al.  Spatial spectral analysis of human electrocorticograms including the alpha and gamma bands , 2000, Journal of Neuroscience Methods.

[11]  Warren M Grill,et al.  Analysis of deep brain stimulation electrode characteristics for neural recording , 2014, Journal of neural engineering.

[12]  L. Miller,et al.  Optimal spacing of surface electrode arrays for brain–machine interface applications , 2010, Journal of neural engineering.

[13]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[14]  E. Fetz,et al.  Operant Conditioning of Specific Patterns of Neural and Muscular Activity , 1971, Science.

[15]  Bijan Pesaran,et al.  Uncovering the Mysterious Origins of Local Field Potentials , 2009, Neuron.

[16]  J. A. Wilson,et al.  Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. , 2007, Journal of neurosurgery.

[17]  G. Schalk,et al.  ECoG factors underlying multimodal control of a brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  E. Halgren,et al.  High-frequency neural activity and human cognition: Past, present and possible future of intracranial EEG research , 2012, Progress in Neurobiology.

[19]  W. Walter,et al.  COMPARISON OF SUBCORTICAL, CORTICAL AND SCALP ACTIVITY USING CHRONICALLY INDWELLING ELECTRODES IN MAN. , 1965, Electroencephalography and clinical neurophysiology.

[20]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[21]  Zachary V Freudenburg,et al.  Decoding Motor Signals From the Pediatric Cortex: Implications for Brain-Computer Interfaces in Children , 2011, Pediatrics.

[22]  Erik Edwards,et al.  Comparison of time-frequency responses and the event-related potential to auditory speech stimuli in human cortex. , 2009, Journal of neurophysiology.

[23]  Gaute T. Einevoll,et al.  Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential , 2013, PLoS Comput. Biol..

[24]  Adam G. Rouse,et al.  Neural adaptation of epidural electrocorticographic (EECoG) signals during closed-loop brain computer interface (BCI) tasks , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Nicolas Y. Masse,et al.  Advantages of closed-loop calibration in intracortical brain–computer interfaces for people with tetraplegia , 2013, Journal of neural engineering.

[26]  M. Mishkin,et al.  Spontaneous High-Gamma Band Activity Reflects Functional Organization of Auditory Cortex in the Awake Macaque , 2012, Neuron.

[27]  Rajesh P. N. Rao,et al.  Robust, long-term control of an electrocorticographic brain-computer interface with fixed parameters. , 2009, Neurosurgical focus.

[28]  R. Lesser,et al.  Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. , 1998, Brain : a journal of neurology.

[29]  John P. Cunningham,et al.  A High-Performance Neural Prosthesis Enabled by Control Algorithm Design , 2012, Nature Neuroscience.

[30]  L. Miller,et al.  Decoding the rat forelimb movement direction from epidural and intracortical field potentials , 2011, Journal of neural engineering.

[31]  Rajesh P. N. Rao,et al.  Spectral Changes in Cortical Surface Potentials during Motor Movement , 2007, The Journal of Neuroscience.

[32]  D.A. Heldman,et al.  Local field potential spectral tuning in motor cortex during reaching , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  A B Schwartz,et al.  Motor cortical representation of speed and direction during reaching. , 1999, Journal of neurophysiology.

[34]  Bijan Pesaran.  Neural correlations, decisions, and actions , 2010, Current Opinion in Neurobiology.

[35]  Jose M. Carmena,et al.  Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control , 2014, Neuron.

[36]  G. Schalk,et al.  Brain-Computer Interfaces Using Electrocorticographic Signals , 2011, IEEE Reviews in Biomedical Engineering.

[37]  D.M. Taylor,et al.  Information conveyed through brain-control: cursor versus robot , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[38]  Bradley Greger,et al.  Decoding spoken words using local field potentials recorded from the cortical surface , 2010, Journal of neural engineering.

[39]  M. Siegel,et al.  A framework for local cortical oscillation patterns , 2011, Trends in Cognitive Sciences.

[40]  Kristofer E. Bouchard,et al.  Functional Organization of Human Sensorimotor Cortex for Speech Articulation , 2013, Nature.

[41]  D J Weber,et al.  Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[42]  J. Ebersole.  Defining epileptogenic foci: past, present, future. , 1997, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[43]  A. Engel,et al.  Beta-band oscillations—signalling the status quo? , 2010, Current Opinion in Neurobiology.

[44]  W. Singer,et al.  The gamma cycle , 2007, Trends in Neurosciences.

[45]  Gerwin Schalk,et al.  A brain–computer interface using electrocorticographic signals in humans , 2004, Journal of neural engineering.

[46]  Daryl R Kipke,et al.  Theoretical analysis of intracortical microelectrode recordings , 2011, Journal of neural engineering.

[47]  D. Thomson,et al.  Spectrum estimation and harmonic analysis , 1982, Proceedings of the IEEE.

[48]  Brian Litt,et al.  Flexible, Foldable, Actively Multiplexed, High-Density Electrode Array for Mapping Brain Activity in vivo , 2011, Nature Neuroscience.

[49]  Spencer Kellis,et al.  Multi-scale analysis of neural activity in humans : implications for microscale electrocorticography , 2016 .

[50]  R. Eckhorn,et al.  Amplitude envelope correlation detects coupling among incoherent brain signals , 2000, Neuroreport.

[51]  J. A. Wilson,et al.  Two-dimensional movement control using electrocorticographic signals in humans , 2008, Journal of neural engineering.

[52]  Robert T. Knight,et al.  Spatiotemporal imaging of cortical activation during verb generation and picture naming , 2010, NeuroImage.

[53]  E. Niebur,et al.  Neural Correlates of High-Gamma Oscillations (60–200 Hz) in Macaque Local Field Potentials and Their Potential Implications in Electrocorticography , 2008, The Journal of Neuroscience.

[54]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[55]  D W Moran,et al.  A chronic generalized bi-directional brain–machine interface , 2011, Journal of neural engineering.

[56]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[57]  David T. Bundy,et al.  Microscale recording from human motor cortex: implications for minimally invasive electrocorticographic brain-computer interfaces. , 2009, Neurosurgical focus.

[58]  V. Mountcastle.  Modality and topographic properties of single neurons of cat's somatic sensory cortex. , 1957, Journal of neurophysiology.

[59]  M. Cohen,et al.  Measuring and interpreting neuronal correlations , 2011, Nature Neuroscience.

[60]  Rajesh P. N. Rao,et al.  Generalized Features for Electrocorticographic BCIs , 2008, IEEE Transactions on Biomedical Engineering.

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

[62]  T. Womelsdorf,et al.  The role of neuronal synchronization in selective attention , 2007, Current Opinion in Neurobiology.

[63]  G. Elston.  Cortex, cognition and the cell: new insights into the pyramidal neuron and prefrontal function. , 2003, Cerebral cortex.

[64]  Randy L. Buckner,et al.  Unrest at rest: Default activity and spontaneous network correlations , 2007, NeuroImage.

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

[66]  E. Fetz,et al.  Volitional control of single cortical neurons in a brain–machine interface , 2011, Journal of neural engineering.

[67]  Adam G Rouse,et al.  Differentiating closed-loop cortical intention from rest: building an asynchronous electrocorticographic BCI , 2013, Journal of neural engineering.

[68]  Shuiwang Ji,et al.  SLEP: Sparse Learning with Efficient Projections , 2011 .

[69]  E. F. Chang,et al.  Sub-centimeter language organization in the human temporal lobe , 2011, Brain and Language.