Single-Trial Decoding of Motion Direction During Visual Attention From Local Field Potential Signals

Brain-Computer Interface (BCI) based on Local Field Potential (LFP) has recently been developed to restore communication or behavioral functions. LFP provides comprehensive information, due to its stability, robustness, and reach frequency content within the cognitive process. It has been demonstrated that spatial attention can be decoded from brain activity in the visual cortical areas. However, whether motion direction can be decoded from the LFP signal in the primate visual cortex remains uninvestigated, as well as how decoding performance may be influenced by spatial attention. In this paper, these issues were examined by recording LFP from the middle temporal area (MT) of macaque, employing machine learning algorithms. The animal was trained to report a brief direction change in a target stimulus which moved in various directions during a visual attention task. It was found that the LFP-gamma power was able to provide significant information to reliably decode motion direction, compared with other frequency bands, on a single-trial basis. Moreover, the results show that spatial attention leads to enhancements in motion direction discrimination performance. The highest decoding performance was achieved in the high-gamma frequencies (60–120Hz) when targets were presented inside the receptive field in opposite directions. Using a feature selection approach, performance was improved by optimally selecting features where the highest level of participation was observed in the gamma-band. Generally, the results suggest that in the MT area, LFP signals exhibit appreciable information about visual features like motion direction, which could thus be utilized as a control signal for cognitive BCI systems.

[1]  Jude F. Mitchell,et al.  Spatial Attention Modulates Center-Surround Interactions in Macaque Visual Area V4 , 2009, Neuron.

[2]  Timothy G. Constandinou,et al.  Impact of referencing scheme on decoding performance of LFP-based brain-machine interface , 2020, bioRxiv.

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

[4]  Ji-Hoon Kim,et al.  High-Speed Visual Target Identification for Low-Cost Wearable Brain-Computer Interfaces , 2019, IEEE Access.

[5]  Hyungmin Kim,et al.  Classification of Selective Attention Within Steady-State Somatosensory Evoked Potentials From Dry Electrodes Using Mutual Information-Based Spatio-Spectral Feature Selection , 2020, IEEE Access.

[6]  Peter M. Kaskan,et al.  Orientation and Direction-of-Motion Response in the Middle Temporal Visual Area (MT) of New World Owl Monkeys as Revealed by Intrinsic-Signal Optical Imaging , 2010, Frontiers in Neuroanatomy.

[7]  Pierre Baraduc,et al.  Direct Two-Dimensional Access to the Spatial Location of Covert Attention in Macaque Prefrontal Cortex , 2016, Current Biology.

[8]  Juvenal Rodríguez-Reséndiz,et al.  A New Approach for Motor Imagery Classification Based on Sorted Blind Source Separation, Continuous Wavelet Transform, and Convolutional Neural Network , 2019, Sensors.

[9]  Mohammad Reza Daliri A hybrid method for the decoding of spatial attention using the MEG brain signals , 2014, Biomed. Signal Process. Control..

[10]  Robert D Flint,et al.  Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control , 2016, The Journal of Neuroscience.

[11]  Michael J. Black,et al.  Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array , 2011 .

[12]  David J. Freedman,et al.  Interaction between Spatial and Feature Attention in Posterior Parietal Cortex , 2016, Neuron.

[13]  Ahmed H. Tewfik,et al.  High Accuracy Decoding of Movement Target Direction in Non-Human Primates Based on Common Spatial Patterns of Local Field Potentials , 2010, PloS one.

[14]  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.

[15]  S. Treue,et al.  Feature-Based Attention Increases the Selectivity of Population Responses in Primate Visual Cortex , 2004, Current Biology.

[16]  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.

[17]  Sébastien Tremblay,et al.  Single-Trial Decoding of Visual Attention from Local Field Potentials in the Primate Lateral Prefrontal Cortex Is Frequency-Dependent , 2015, The Journal of Neuroscience.

[18]  D. Heeger,et al.  The Normalization Model of Attention , 2009, Neuron.

[19]  A. Engel,et al.  An independent brain–computer interface using covert non-spatial visual selective attention , 2010, Journal of neural engineering.

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

[21]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

[22]  T D Albright,et al.  Effect of feature-selective attention on neuronal responses in macaque area MT. , 2012, Journal of neurophysiology.

[23]  Sébastien Tremblay,et al.  Attentional Filtering of Visual Information by Neuronal Ensembles in the Primate Lateral Prefrontal Cortex , 2015, Neuron.

[24]  R. Desimone,et al.  Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention , 2001, Science.

[25]  S. Treue,et al.  Strategic deployment of feature-based attentional gain in primate visual cortex , 2019, PLoS biology.

[26]  Stefan Treue,et al.  Combining spatial and feature-based attention within the receptive field of MT neurons , 2009, Vision Research.

[27]  R. Desimone,et al.  Laminar differences in gamma and alpha coherence in the ventral stream , 2011, Proceedings of the National Academy of Sciences.

[28]  Guangsheng Liang,et al.  Limited interactions between space- and feature-based attention in visually sparse displays , 2020, Journal of vision.

[29]  R. Desimone,et al.  High-Frequency, Long-Range Coupling Between Prefrontal and Visual Cortex During Attention , 2009, Science.

[30]  Shy Shoham,et al.  Cognitive-motor brain–machine interfaces , 2014, Journal of Physiology-Paris.

[31]  W. Newsome,et al.  Local Field Potential in Cortical Area MT: Stimulus Tuning and Behavioral Correlations , 2006, The Journal of Neuroscience.

[32]  Mohammad Reza Daliri,et al.  Evaluation of local field potential signals in decoding of visual attention , 2015, Cognitive Neurodynamics.

[33]  R. Vogels,et al.  Decoding of Repeated Objects from Local Field Potentials in Macaque Inferior Temporal Cortex , 2013, PloS one.

[34]  Martin Vinck,et al.  Attentional Modulation of Cell-Class-Specific Gamma-Band Synchronization in Awake Monkey Area V4 , 2013, Neuron.

[35]  Farran Briggs,et al.  Attentional Modulation of Neuronal Activity Depends on Neuronal Feature Selectivity , 2017, Current Biology.

[36]  Sunita Mandon,et al.  Toward High Performance, Weakly Invasive Brain Computer Interfaces Using Selective Visual Attention , 2013, The Journal of Neuroscience.

[37]  Paul S Khayat,et al.  Frequency-Dependent Attentional Modulation of Local Field Potential Signals in Macaque Area MT , 2010, The Journal of Neuroscience.

[38]  Mohammad Reza Daliri,et al.  Decoding covert visual attention based on phase transfer entropy , 2020, Physiology & Behavior.

[39]  Selina S. Solomon,et al.  Integration and segregation of multiple motion signals by neurons in area MT of primate. , 2014, Journal of neurophysiology.

[40]  A. Burkitt,et al.  Pattern Motion Processing by MT Neurons , 2019, Front. Neural Circuits.

[41]  Mohammad Reza Daliri,et al.  Decoding of Visual Attention from LFP Signals of Macaque MT , 2014, PloS one.

[42]  L Tonin,et al.  An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation , 2013, Journal of neural engineering.

[43]  Detlef Wegener,et al.  Task-specific, dimension-based attentional shaping of motion processing in monkey area MT. , 2017, Journal of neurophysiology.

[44]  W. Freiwald,et al.  Coherent oscillatory activity in monkey area v4 predicts successful allocation of attention. , 2005, Cerebral cortex.

[45]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[46]  Julio Martinez-Trujillo,et al.  A Normalization Circuit Underlying Coding of Spatial Attention in Primate Lateral Prefrontal Cortex , 2019, eNeuro.

[47]  John H. R. Maunsell,et al.  Feature-based attention in visual cortex , 2006, Trends in Neurosciences.

[48]  R. Desimone,et al.  The Effects of Visual Stimulation and Selective Visual Attention on Rhythmic Neuronal Synchronization in Macaque Area V4 , 2008, The Journal of Neuroscience.

[49]  Gérard Dreyfus,et al.  A cognitive brain–computer interface monitoring sustained attentional variations during a continuous task , 2019, Cognitive Neurodynamics.

[50]  Jun Dai,et al.  Using High-Frequency Local Field Potentials From Multicortex to Decode Reaching and Grasping Movements in Monkey , 2019, IEEE Transactions on Cognitive and Developmental Systems.

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

[52]  J. Bisley The neural basis of visual attention , 2011, The Journal of physiology.

[53]  J. Maunsell,et al.  Effects of Attention on the Processing of Motion in Macaque Middle Temporal and Medial Superior Temporal Visual Cortical Areas , 1999, The Journal of Neuroscience.

[54]  M. Carrasco Visual attention: The past 25 years , 2011, Vision Research.

[55]  J. Maunsell,et al.  Using Neuronal Populations to Study the Mechanisms Underlying Spatial and Feature Attention , 2011, Neuron.

[56]  Zeki Oralhan,et al.  3D Input Convolutional Neural Networks for P300 Signal Detection , 2020, IEEE Access.

[57]  L. Busse,et al.  Attention to the Color of a Moving Stimulus Modulates Motion-Signal Processing in Macaque Area MT: Evidence for a Unified Attentional System , 2009, Front. Syst. Neurosci..

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

[59]  Thomas Serre,et al.  Object decoding with attention in inferior temporal cortex , 2011, Proceedings of the National Academy of Sciences.

[60]  Zeki Oralhan,et al.  A New Paradigm for Region-Based P300 Speller in Brain Computer Interface , 2019, IEEE Access.

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

[62]  John H. R. Maunsell,et al.  Attention to both space and feature modulates neuronal responses in macaque area V4. , 2000, Journal of neurophysiology.

[63]  J. Gallant,et al.  Combined effects of spatial and feature-based attention on responses of V4 neurons , 2009, Vision Research.

[64]  Stefan Treue,et al.  Feature-based attention influences motion processing gain in macaque visual cortex , 1999, Nature.

[65]  Louise S. Delicato,et al.  Attention Reduces Stimulus-Driven Gamma Frequency Oscillations and Spike Field Coherence in V1 , 2010, Neuron.

[66]  Geraint Rees,et al.  Real-time decoding of covert attention in higher-order visual areas , 2018, NeuroImage.

[67]  Gerhard Friehs,et al.  Intra-day signal instabilities affect decoding performance in an intracortical neural interface system , 2013, Journal of neural engineering.

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