Document Type : Full Research Paper


1 Instructor, Department of Biomedical Engineering, Faculty of Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Professor, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

3 Professor, Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran



Visual attention as a cognitive factor plays a significant role in the processing of higher-order mental information that happens in the brain and affects brain activity in various areas of the visual cortex. Among the various recording systems, local field potentials, due to their stability, robustness, and frequency content have received interest in brain structure and cognitive processing research, as well as brain-computer interface (BCI) systems. Hence, the extraction and interpretation of information from local field potential (LFP) signals during visual attention has been considered to control cognitive systems. Cross-frequency coupling (CFC) as one of the information encoding strategies in the brain plays a functional role in perception, working memory, and visual attention tasks. However, the role of CFC as informative features for spatial attention decoding has not been adequately investigated. This paper aims to examine spatial attention decoding using LFP signals recorded from the monkey middle temporal area (MT). For this purpose, phase-phase and phase-amplitude coupling features and machine learning algorithms have been employed. The results show that the highest decoding performance was achieved by applying selected optimal features and the support vector machine classifier (90.36%). Moreover, among the selected features, gamma-delta, gamma-alpha, and beta-delta coupling contain the most cognitive information and the most effective features to improve the decoding performance of spatial attention in the visual system. Generally, the results suggest that cross-frequency coupling of LFP signals contains significant information in spatial attention tasks, and can be used as a suitable alternative to the time-frequency features of brain signals in cognitive BCI systems.


Main Subjects

  1. Carrasco, “Visual attention: The past 25 years,” Vision Res., vol. 51, no. 13, pp. 1484–1525, 2011.
  2. R. Cohen and J. H. R. Maunsell, “Using neuronal populations to study the mechanisms underlying spatial and feature attention,” Neuron, vol. 70, no. 6, pp. 1192–1204, 2011.
  3. S. Khayat, R. Niebergall, and J. C. Martinez-Trujillo, “Frequency-dependent attentional modulation of local field potential signals in macaque area MT,” J. Neurosci., vol. 30, no. 20, pp. 7037–7048, 2010.
  4. Ekanayake, C. Hutton, G. Ridgway, F. Scharnowski, N. Weiskopf, and G. Rees, “Real-time decoding of covert attention in higher-order visual areas,” Neuroimage, vol. 169, pp. 462–472, 2018.
  5. Ahmadi, S. Davoudi, M. Behroozi, and M. R. Daliri, “Decoding covert visual attention based on phase transfer entropy,” Physiol. Behav., vol. 222, p. 112932, 2020.
  6. Gaume, G. Dreyfus, and F.-B. Vialatte, “A cognitive brain--computer interface monitoring sustained attentional variations during a continuous task,” Cogn. Neurodyn., vol. 13, no. 3, pp. 257–269, 2019.
  7. R. Daliri, “A hybrid method for the decoding of spatial attention using the MEG brain signals,” Biomed. Signal Process. Control, vol. 10, pp. 308–312, 2014.
  8. Zhang, A. Maye, X. Gao, B. Hong, A. K. Engel, and S. Gao, “An independent brain--computer interface using covert non-spatial visual selective attention,” J. Neural Eng., vol. 7, no. 1, p. 16010, 2010.
  9. Tonin, R. Leeb, A. Sobolewski, and J. Del R Millán, “An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation,” J. Neural Eng., vol. 10, no. 5, p. 56007, 2013.
  10. Oralhan, “A new paradigm for region-based P300 speller in brain computer interface,” Ieee Access, vol. 7, pp. 106618–106627, 2019.
  11. J. Ortiz-Echeverri et al., “A new approach for motor imagery classification based on sorted blind source separation, continuous wavelet transform, and convolutional neural network,” Sensors, vol. 19, no. 20, p. 4541, 2019.
  12. Milekovic et al., “Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals,” J. Neurophysiol., vol. 120, no. 7, pp. 343–360, 2018.
  13. A. Perge et al., “Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex,” J. Neural Eng., vol. 11, no. 4, p. 46007, 2014.
  14. Buzsáki, C. A. Anastassiou, and C. Koch, “The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes,” Nat. Rev. Neurosci., vol. 13, no. 6, pp. 407–420, 2012.
  15. W. Bisley, “The neural basis of visual attention,” J. Physiol., vol. 589, no. 1, pp. 49–57, 2011.
  16. Fries, J. H. Reynolds, A. E. Rorie, and R. Desimone, “Modulation of oscillatory neuronal synchronization by selective visual attention,” Science (80-. )., vol. 291, no. 5508, pp. 1560–1563, 2001.
  17. Fries, T. Womelsdorf, R. Oostenveld, and R. Desimone, “The effects of visual stimulation and selective visual attention on rhythmic neuronal synchronization in macaque area V4,” J. Neurosci., vol. 28, no. 18, pp. 4823–4835, 2008.
  18. Vinck et al., “Attentional modulation of cell-class-specific gamma-band synchronization in awake monkey area v4,” Neuron, vol. 80, no. 4, pp. 1077–1089, 2013.
  19. Schledde et al., “Task-specific, dimension-based attentional shaping of motion processing in monkey area MT,” J. Neurophysiol., vol. 118, no. 3, pp. 1542–1555, 2017.
  20. Kozyrev, M. R. Daliri, P. Schwedhelm, and S. Treue, “Strategic deployment of feature-based attentional gain in primate visual cortex,” PLoS Biol., vol. 17, no. 8, p. e3000387, 2019.
  21. R. Hembrook-Short, V. L. Mock, and F. Briggs, “Attentional modulation of neuronal activity depends on neuronal feature selectivity,” Curr. Biol., vol. 27, no. 13, pp. 1878–1887, 2017.
  22. H. Reynolds and D. J. Heeger, “The normalization model of attention,” Neuron, vol. 61, no. 2, pp. 168–185, 2009.
  23. A. Sundberg, J. F. Mitchell, and J. H. Reynolds, “Spatial attention modulates center-surround interactions in macaque visual area v4,” Neuron, vol. 61, no. 6, pp. 952–963, 2009.
  24. C. Martinez-Trujillo and S. Treue, “Feature-based attention increases the selectivity of population responses in primate visual cortex,” Curr. Biol., vol. 14, no. 9, pp. 744–751, 2004.
  25. Liu and I. Mance, “Constant spread of feature-based attention across the visual field,” Vision Res., vol. 51, no. 1, pp. 26–33, 2011.
  26. J. McAdams and J. H. R. Maunsell, “Attention to both space and feature modulates neuronal responses in macaque area V4,” J. Neurophysiol., vol. 83, no. 3, pp. 1751–1755, 2000.
  27. Katzner, L. Busse, and S. Treue, “Attention to the color of a moving stimulus modulates motion-signal processing in macaque area MT: evidence for a unified attentional system,” Front. Syst. Neurosci., vol. 3, p. 12, 2009.
  28. Ibos and D. J. Freedman, “Interaction between spatial and feature attention in posterior parietal cortex,” Neuron, vol. 91, no. 4, pp. 931–943, 2016.
  29. R. Patzwahl and S. Treue, “Combining spatial and feature-based attention within the receptive field of MT neurons,” Vision Res., vol. 49, no. 10, pp. 1188–1193, 2009.
  30. R. Nazari, A. M. Nasrabadi, and M. R. Daliri, “Single-Trial Decoding of Motion Direction During Visual Attention From Local Field Potential Signals,” IEEE Access, vol. 9, pp. 66450–66461, 2021.
  31. A. Kaliukhovich and R. Vogels, “Decoding of repeated objects from local field potentials in macaque inferior temporal cortex,” PLoS One, vol. 8, no. 9, p. e74665, 2013.
  32. Tremblay, G. Doucet, F. Pieper, A. Sachs, and J. Martinez-Trujillo, “Single-trial decoding of visual attention from local field potentials in the primate lateral prefrontal cortex is frequency-dependent,” J. Neurosci., vol. 35, no. 24, pp. 9038–9049, 2015.
  33. Wang et al., “Long-term decoding stability of local field potentials from silicon arrays in primate motor cortex during a 2D center out task,” J. Neural Eng., vol. 11, no. 3, p. 36009, 2014.
  34. D. Flint, E. W. Lindberg, L. R. Jordan, L. E. Miller, and M. W. Slutzky, “Accurate decoding of reaching movements from field potentials in the absence of spikes,” J. Neural Eng., vol. 9, no. 4, p. 46006, 2012.
  35. W. Slutzky, L. R. Jordan, E. W. Lindberg, K. E. Lindsay, and L. E. Miller, “Decoding the rat forelimb movement direction from epidural and intracortical field potentials,” J. Neural Eng., vol. 8, no. 3, p. 36013, 2011.
  36. Seif and M. R. Daliri, “Evaluation of local field potential signals in decoding of visual attention,” Cogn. Neurodyn., vol. 9, no. 5, pp. 509–522, 2015.
  37. Jirsa and V. Müller, “Cross-frequency coupling in real and virtual brain networks,” Front. Comput. Neurosci., vol. 7, p. 78, 2013.
  38. Kaplan et al., “Medial prefrontal theta phase coupling during spatial memory retrieval,” Hippocampus, vol. 24, no. 6, pp. 656–665, 2014.
  39. Jensen, B. Gips, T. O. Bergmann, and M. Bonnefond, “Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing,” Trends Neurosci., vol. 37, no. 7, pp. 357–369, 2014.
  40. Hyafil, A.-L. Giraud, L. Fontolan, and B. Gutkin, “Neural cross-frequency coupling: connecting architectures, mechanisms, and functions,” Trends Neurosci., vol. 38, no. 11, pp. 725–740, 2015.
  41. T. Canolty et al., “High gamma power is phase-locked to theta oscillations in human neocortex,” Science (80-. )., vol. 313, no. 5793, pp. 1626–1628, 2006.
  42. T. Canolty and R. T. Knight, “The functional role of cross-frequency coupling,” Trends Cogn. Sci., vol. 14, no. 11, pp. 506–515, 2010.
  43. Axmacher, M. M. Henseler, O. Jensen, I. Weinreich, C. E. Elger, and J. Fell, “Cross-frequency coupling supports multi-item working memory in the human hippocampus,” Proc. Natl. Acad. Sci., vol. 107, no. 7, pp. 3228–3233, 2010.
  44. Whittingstall and N. K. Logothetis, “Frequency-band coupling in surface EEG reflects spiking activity in monkey visual cortex,” Neuron, vol. 64, no. 2, pp. 281–289, 2009.
  45. Spaak, M. Bonnefond, A. Maier, D. A. Leopold, and O. Jensen, “Layer-specific entrainment of gamma-band neural activity by the alpha rhythm in monkey visual cortex,” Curr. Biol., vol. 22, no. 24, pp. 2313–2318, 2012.
  46. B. L. Tort, R. W. Komorowski, J. R. Manns, N. J. Kopell, and H. Eichenbaum, “Theta--gamma coupling increases during the learning of item--context associations,” Proc. Natl. Acad. Sci., vol. 106, no. 49, pp. 20942–20947, 2009.
  47. E. Lisman and O. Jensen, “The theta-gamma neural code,” Neuron, vol. 77, no. 6, pp. 1002–1016, 2013.
  48. Raghavachari et al., “Gating of human theta oscillations by a working memory task,” J. Neurosci., vol. 21, no. 9, pp. 3175–3183, 2001.
  49. X. Cohen, C. E. Elger, and J. Fell, “Oscillatory activity and phase--amplitude coupling in the human medial frontal cortex during decision making,” J. Cogn. Neurosci., vol. 21, no. 2, pp. 390–402, 2008.
  50. Yanagisawa et al., “Regulation of motor representation by phase--amplitude coupling in the sensorimotor cortex,” J. Neurosci., vol. 32, no. 44, pp. 15467–15475, 2012.
  51. Tass et al., “Detection of n:m Phase Locking from Noisy Data: Application to Magnetoencephalography,” Phys. Rev. Lett., vol. 81, no. 15, pp. 3291–3294, Oct. 1998, doi: 10.1103/PhysRevLett.81.3291.
  52. Scheffer-Teixeira and A. B. L. Tort, “On cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus,” Elife, vol. 5, p. e20515, 2016.
  53. Zheng, K. W. Bieri, Y.-T. Hsiao, and L. L. Colgin, “Spatial sequence coding differs during slow and fast gamma rhythms in the hippocampus,” Neuron, vol. 89, no. 2, pp. 398–408, 2016.
  54. J. Soltanzadeh and M. R. Daliri, “Evaluation of phase locking and cross correlation methods for estimating the time lag between brain sites: A simulation approach,” Basic Clin. Neurosci., vol. 5, no. 3, p. 205, 2014.
  55. D. Penny, E. Duzel, K. J. Miller, and J. G. Ojemann, “Testing for nested oscillation,” J. Neurosci. Methods, vol. 174, no. 1, pp. 50–61, 2008.
  56. -P. Lachaux, E. Rodriguez, J. Martinerie, and F. J. Varela, “Measuring phase synchrony in brain signals,” Hum. Brain Mapp., vol. 8, no. 4, pp. 194–208, 1999.
  57. B. Kotsiantis et al., “Supervised machine learning: A review of classification techniques,” Emerg. Artif. Intell. Appl. Comput. Eng., vol. 160, no. 1, pp. 3–24, 2007.
  58. Srivastava, M. R. Gupta, and B. A. Frigyik, “Bayesian quadratic discriminant analysis.,” J. Mach. Learn. Res., vol. 8, no. 6, 2007.
  59. W. Kim, J. Lee, H.-J. Kim, Y. S. Lee, and K. J. Min, “Relationship between theta-phase gamma-amplitude coupling and attention-deficit/hyperactivity behavior in children,” Neurosci. Lett., vol. 590, pp. 12–17, 2015.
  60. Jafakesh, F. Z. Jahromy, and M. R. Daliri, “Decoding of object categories from brain signals using cross frequency coupling methods,” Biomed. Signal Process. Control, vol. 27, pp. 60–67, 2016.
  61. Davoudi, A. Ahmadi, and M. R. Daliri, “Frequency--amplitude coupling: a new approach for decoding of attended features in covert visual attention task,” Neural Comput. Appl., pp. 1–16, 2020.