Publications

[Original researches of data] 
  1. Canales-Johnson, A., Borges, A. F. T., Komatsu, M., Fujii, N., Fahrenfort, J. J., Miller, K. J., & Noreika, V. (2021). Broadband Dynamics Rather than Frequency-Specific Rhythms Underlie Prediction Error in the Primate Auditory Cortex. Journal of Neuroscience, 41(45), 9374-9391.
  2. Komatsu, M., & Ichinohe, N. (2020). Effects of ketamine administration on auditory information processing in the neocortex of nonhuman primates. Frontiers in psychiatry, 826.
  3. Komatsu, M., Sugano, E., Tomita, H., & Fujii, N. (2017). A chronically implantable bidirectional neural interface for non-human primates. Frontiers in Neuroscience, 11:514.
  4. Oosugi, N., Kitajo, K., Hasegawa, N., Nagasaka, Y., Okanoya, K., & Fujii, N. (2017). A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal. Neural Networks, 93, 1-6.
  5. Oosugi, N., Yanagawa, T., Nagasaka, Y., & Fujii, N. (2016). Social Suppressive Behavior Is Organized by the Spatiotemporal Integration of Multiple Cortical Regions in the Japanese Macaque. PloS one, 11(3), e0150934.
  6. Chao, Z. C., Nagasaka, Y., & Fujii, N. (2015). Cortical network architecture for context processing in primate brain. eLife, 4, e06121.
  7. Komatsu, M., Takaura, K., & Fujii, N. (2015). Mismatch negativity in common marmosets: Whole-cortical recordings with multi-channel electrocorticograms. Scientific reports, 5, 15006.
  8. Yanagawa, T., Chao, Z. C., Hasegawa, N., & Fujii, N. (2013). "Large-Scale Information Flow in Conscious and Unconscious States: an ECoG Study in Monkeys." PloS one, 8(11), e80845.
  9. Chao ZC, Fujii N (2013). "Mining spatio-spectro-temporal cortical dynamics: a guideline for offline and online electrocorticographic analyses." in Advanced Methods in Neuroethological Research, Hiroto Ogawa and Kotaro Oka, editors, Springer, 39-55.
  10. Shimoda K, Nagasaka Y, Chao ZC, Fujii N (2012). "Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques." J. Neural Eng. 9:036015.
  11. Nagasaka Y, Shimoda K, Fujii N (2011). "Multidimensional recording (MDR) and data sharing: an ecological open research and educational platform for neuroscience." PLOS ONE 6(7):e22561.
  12. Chao ZC, Nagasaka Y, Fujii N (2010). "Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys." Frontiers in Neuroengineering 3:3. doi:10.3389/fneng.2010.00003.

[Applications] 
  1. Tsurugizawa, T., Komaki, Y., Aota, I., Suematsu, M., et al. (2025). A cross-species brain magnetic resonance imaging and histology database of vertebrates. Scientific Data.
  2. Takahashi, Y., Idei, H., Komatsu, M., Tani, J., Tomita, H., et al. (2025). Digital twin brain simulator for real-time consciousness monitoring and virtual intervention using primate electrocorticogram data. npj Digital Medicine.
  3. Wang, J., Wang, Z., Xu, T., Si, Y., Li, A., Zhou, T., et al. (2025). Bridging BCI and Communications: A MIMO Framework for EEG-to-ECoG Wireless Channel Modeling. IEEE Wireless Communications Letters.
  4. Li, Y., Chen, B., Bai, W., Koike, Y., Yamashita, O. (2025). Robust Sparse Bayesian Learning Based on Minimum Error Entropy for Noisy High-Dimensional Brain Activity Decoding. arXiv.
  5. Du, W., Huang, H. (2025). Response function as a quantitative measure of consciousness in brain dynamics. arXiv.
  6. Ivey, V., Yuan, H., Ding, L. (2025). Time-resolved large-scale neural coactivations in macaque monkey. NeuroImage.
  7. Balsells-Rodas, C., Wang, Y., Mediano, P. A. M., et al. (2024). Identifying nonstationary causal structures with high-order markov switching models. arXiv.
  8. Vetter, J., Macke, J. H., & Gao, R. (2024). Generating realistic neurophysiological time series with denoising diffusion probabilistic models. Patterns.
  9. Shimaoka, D., Leung, A., Price, N., Banks, M., Nourski, K., et al. (2024). Registered report: common signatures of loss of consciousness in human and macaque electrocorticogram. OSF.
  10. Gelens, F., Aijala, J., Roberts, L., Komatsu, M., et al. (2024). Distributed representations of prediction error signals across the cortical hierarchy are synergistic. Nature Communications.
  11. Villela, V. C. (2024). Statistical Methods for Directed Graphs Basead on the Graph Spectrum.
  12. Einizade, A., & Sardouie, S. H. (2023). Iterative Pseudo-Sparse Partial Least Square and Its Higher Order Variant: Application to Inference from High-Dimensional Biosignals. IEEE Transactions on Cognitive and Developmental Systems, 16(1), 296-307.
  13. Paz-Linares, D., Gonzalez-Moreira, E., Areces-Gonzalez, A., Wang, Y., Li, M., Martinez-Montes, E., ... & Valdes-Sosa, P. A. (2023). Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG. Scientific Reports, 13(1), 11466.
  14. Vetter, J., Macke, J. H., & Gao, R. (2023). Generating realistic neurophysiological time series with denoising diffusion probabilistic models. bioRxiv, 2023-08.
  15. Li, A., Liu, H., Lei, X., He, Y., Wu, Q., Yan, Y., ... & Liu, B. (2023). Hierarchical fluctuation shapes a dynamic flow linked to states of consciousness. Nature communications, 14(1), 3238.
  16. Paz-Linares, D., Gonzalez-Moreira, E., Areces-Gonzalez, A., Wang, Y., Li, M., Vega-Hernandez, M., ... & Valdes-Sosa, P. A. (2023). Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning. Frontiers in Neuroscience, 17, 978527.
  17. Wodeyar, A., Marshall, F. A., Chu, C. J., Eden, U. T., & Kramer, M. A. (2023). Different Methods to Estimate the Phase of Neural Rhythms Agree But Only During Times of Low Uncertainty. Eneuro, 10(11).
  18. Ramos, T. C., Mourão-Miranda, J., & Fujita, A. (2023). Spectral density-based clustering algorithms for complex networks. Frontiers in Neuroscience, 17, 926321.
  19. Li, Y., Chen, B., Wang, G., Yoshimura, N., & Koike, Y. (2023). Partial maximum correntropy regression for robust electrocorticography decoding. Frontiers in Neuroscience, 17, 1213035.
  20. Villela, V. C., Lira, E. S., & Fujita, A. (2023, September). Check for updates Spectrum-Based Statistical Methods for Directed Graphs with Applications in Biological Data. In Advances in Bioinformatics and Computational Biology: 16th Brazilian Symposium on Bioinformatics, BSB 2023, Curitiba, Brazil, June 13–16, 2023, Proceedings (Vol. 13954, p. 46). Springer Nature.
  21. Branco, M. P., Geukes, S. H., Aarnoutse, E. J., Ramsey, N. F., & Vansteensel, M. J. (2023). Nine decades of electrocorticography: A comparison between epidural and subdural recordings. European Journal of Neuroscience, 57(8), 1260-1288.
  22. Medel, V., Irani, M., Crossley, N., Ossandón, T., & Boncompte, G. (2023). Complexity and 1/f slope jointly reflect brain states. Scientific Reports, 13(1), 21700.
  23. Munn, B. R., Müller, E. J., Medel, V., Naismith, S. L., Lizier, J. T., Sanders, R. D., & Shine, J. M. (2023). Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states. Nature Communications, 14(1), 6846.
  24. Lord, L. D., Carletti, T., Fernandes, H., Turkheimer, F. E., & Expert, P. (2023). Altered dynamical integration/segregation balance during anesthesia-induced loss of consciousness. Frontiers in network physiology, 3, 1279646.
  25. Mediano, P. A., Rosas, F. E., Luppi, A. I., Noreika, V., Seth, A. K., Carhart-Harris, R. L., ... & Bor, D. (2023). Spectrally and temporally resolved estimation of neural signal diversity. bioRxiv, 2023-03.
  26. Parameshwaran, D., & Thiagarajan, T. C. (2023). High Variability Periods in the EEG Distinguish Cognitive Brain States. Brain Sciences, 13(11), 1528.
  27. Xie, T., Wu, Z., Foutz, T. J., Sheng, X., Zhu, X., Leuthardt, E. C., ... & Brunner, P. (2023). Slow-wave modulation analysis during states of unconsciousness using the novel tau-modulation method. Journal of Neural Engineering, 20(4), 046013.
  28. de la Fuente, L. A., Zamberlan, F., Bocaccio, H., Kringelbach, M., Deco, G., Perl, Y. S., ... & Tagliazucchi, E. (2023). Temporal irreversibility of neural dynamics as a signature of consciousness. Cerebral Cortex, 33(5), 1856-1865.
  29. Mediano, P. A., Rosas, F. E., Luppi, A. I., Jensen, H. J., Seth, A. K., Barrett, A. B., ... & Bor, D. (2022). Greater than the parts: a review of the information decomposition approach to causal emergence. Philosophical Transactions of the Royal Society A, 380(2227), 20210246.
  30. Faes, L., Mijatovic, G., Antonacci, Y., Pernice, R., Barà, C., Sparacino, L., ... & Stramaglia, S. (2022). A new framework for the time-and frequency-domain assessment of high-order interactions in networks of random processes. IEEE Transactions on Signal Processing, 70, 5766-5777.
  31. Mofrad, M. H., Gilmore, G., Koller, D., Mirsattari, S. M., Burneo, J. G., Steven, D. A., ... & Muller, L. (2022). Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load. Elife, 11, e75769.
  32. Faes, L., Mijatovic, G., Antonacci, Y., Pernice, R., Barà, C., Sparacino, L., ... & Stramaglia, S. (2022). A framework for the time-and frequency-domain assessment of high-order interactions in brain and physiological networks. arXiv preprint arXiv:2202.04179.
  33. Padovani, E. C. (2022). Ketamine-Medetomidine General Anesthesia Occurs With Alternation of Cortical Electrophysiological Activity Between High and Low Complex States. arXiv preprint arXiv:2202.04320.
  34. Isachenko, R. V., & Strijov, V. V. (2022). Quadratic programming feature selection for multicorrelated signal decoding with partial least squares. Expert Systems with Applications, 207, 117967.
  35. Parameshwaran, D., & Thiagarajan, T. (2022). High Variability Periods in the EEG: A New Temporal Metric that Reflects Brain States. bioRxiv, 2022-06.
  36. Deco, G., Sanz Perl, Y., Bocaccio, H., Tagliazucchi, E., & Kringelbach, M. L. (2022). The INSIDEOUT framework provides precise signatures of the balance of intrinsic and extrinsic dynamics in brain states. Communications Biology, 5(1), 572.
  37. Ran, X., Chen, W., Yvert, B., & Zhang, S. (2022). A hybrid autoencoder framework of dimensionality reduction for brain-computer interface decoding. Computers in biology and medicine, 148, 105871.
  38. Chen, K., Xie, T., Ma, L., Hudson, A. E., Ai, Q., & Liu, Q. (2022). A two-stream graph convolutional network based on brain connectivity for anesthetized states analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 2077-2087.
  39. Varley, T. F., & Sporns, O. (2022). Network analysis of time series: Novel approaches to network neuroscience. Frontiers in Neuroscience, 15, 787068.
  40. Fuentes, N., Garcia, A., Guevara, R., Orofino, R., & Mateos, D. M. (2022). Complexity of brain dynamics as a correlate of consciousness in anaesthetized monkeys. Neuroinformatics, 20(4), 1041-1054.
  41. Xie, T., Chen, K., Ma, L., Ai, Q., Liu, Q., & Hudson, A. E. (2021, November). Brain Connectivity Analysis in Anesthetized and Awake States: an ECoG Study in Monkeys. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 117-120). IEEE.
  42. Varley, T. F., Denny, V., Sporns, O., & Patania, A. (2021). Topological analysis of differential effects of ketamine and propofol anaesthesia on brain dynamics. Royal Society open science, 8(6), 201971.
  43. Harris, K. D., Aravkin, A., Rao, R., & Brunton, B. W. (2021). Time-Varying Autoregression with Low-Rank Tensors. SIAM Journal on Applied Dynamical Systems, 20(4), 2335-2358.
  44. Perl, Y. S., Bocaccio, H., Pallavicini, C., Pérez-Ipiña, I., Laureys, S., Laufs, H., ... & Tagliazucchi, E. (2021). Nonequilibrium brain dynamics as a signature of consciousness. Physical Review E, 104(1), 014411.
  45. O’Reilly, J. A. (2021). Roving oddball paradigm elicits sensory gating, frequency sensitivity, and long-latency response in common marmosets. IBRO neuroscience reports, 11, 128-136.
  46. Gu, Y., Sainburg, L. E., Kuang, S., Han, F., Williams, J. W., Liu, Y., ... & Liu, X. (2021). Brain activity fluctuations propagate as waves traversing the cortical hierarchy. Cerebral cortex, 31(9), 3986-4005.
  47. Raut, R. V., Snyder, A. Z., Mitra, A., Yellin, D., Fujii, N., Malach, R., & Raichle, M. E. (2021). Global waves synchronize the brain’s functional systems with fluctuating arousal. Science advances, 7(30), eabf2709.
  48. Wang, J., Tao, A., Anderson, W. S., Madsen, J. R., & Kreiman, G. (2021). Mesoscopic physiological interactions in the human brain reveal small-world properties. Cell Reports, 36(8), 109585.
  49. Popov, T., Miller, G. A., Rockstroh, B., Jensen, O., & Langer, N. (2021). Alpha oscillations link action to cognition: An oculomotor account of the brain's dominant rhythm. bioRxiv.
  50. Li, Y., Chen, B., Wang, G., Yoshimura, N., & Koike, Y. (2021). Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals. arXiv preprint arXiv:2106.13086.
  51. Padovani, E. C. (2021). Macaques Cortical Functional Connectivity Dynamics at the Onset of Propofol-Induced Anesthesia. arXiv preprint arXiv:2108.00371.
  52. Toker, D., Pappas, I., Lendner, J. D., Frohlich, J., Mateos, D. M., Muthukumaraswamy, S., ... & D'Esposito, M. (2021). Consciousness is supported by near-critical cortical electrodynamics. bioRxiv.
  53. Fabietti, M., Mahmud, M., & Lotfi, A. (2020, September). Machine learning in analysing invasively recorded neuronal signals: available open access data sources. In International Conference on Brain Informatics (pp. 151-162). Springer, Cham.
  54. Varley, T. F., Sporns, O., Puce, A., & Beggs, J. (2020). Differential effects of propofol and ketamine on critical brain dynamics. PLoS computational biology, 16(12), e1008418.
  55. Foodeh, R., Ebadollahi, S., & Daliri, M. R. (2020). Regularized partial least square regression for continuous decoding in brain-computer interfaces. Neuroinformatics, 18(3), 465-477.
  56. Gao, R., van den Brink, R. L., Pfeffer, T., & Voytek, B. (2020). Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. Elife, 9, e61277.
  57. Rosas, F. E., Mediano, P. A., Jensen, H. J., Seth, A. K., Barrett, A. B., Carhart-Harris, R. L., & Bor, D. (2020). Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data. PLOS Computational Biology, 16(12), e1008289.
  58. Kitazono, J., Kanai, R., & Oizumi, M. (2020). Efficient search for informational cores in complex systems: Application to brain networks. Neural Networks, 132, 232-244.
  59. Marrouch, N., Slawinska, J., Giannakis, D., & Read, H. L. (2020). Data-driven Koopman operator approach for computational neuroscience. Annals of Mathematics and Artificial Intelligence, 88(11), 1155-1173.
  60. Gao, R. (2020). Bridging Cognition and Neurobiology with Large-Scale Cortical Dynamics and Multimodal Brain Data. University of California, San Diego.
  61. Papadopoulou, M., Friston, K., & Marinazzo, D. (2019). Estimating directed connectivity from cortical recordings and reconstructed sources. Brain topography, 32(4), 741-752.
  62. Wang, Q., Valdés-Hernández, P. A., Paz-Linares, D., Bosch-Bayard, J., Oosugi, N., Komatsu, M., Fujii, N., & Valdés-Sosa, P. A. (2019). EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons—Applications to Connectivity Studies. Brain topography, 1-19.
  63. Marinazzo, D., Riera, J. J., Marzetti, L., Astolfi, L., Yao, D., & Sosa, P. A. V. (2019). Controversies in EEG Source Imaging and Connectivity: Modeling, Validation, Benchmarking.
  64. Agarwal, N., Kathpalia, A., & Nagaraj, N. (2019). Distinguishing Different Levels Of Consciousness using a Novel Network Causal Activity Measure. bioRxiv, 660043.
  65. Halgren, M., Ulbert, I., Bastuji, H., Fabó, D., Erőss, L., Rey, M., ... & Wittner, L. (2019). The generation and propagation of the human alpha rhythm. Proceedings of the National Academy of Sciences.
  66. Todaro, C., Marzetti, L., Sosa, P. A. V., Valdés-Hernandez, P. A., & Pizzella, V. (2019). Mapping brain activity with electrocorticography: resolution properties and robustness of inverse solutions. Brain topography, 32(4), 583-598.
  67. Alonso, L. M., Solovey, G., Yanagawa, T., Proekt, A., Cecchi, G. A., & Magnasco, M. O. (2019). Single-trial classification of awareness state during anesthesia by measuring critical dynamics of global brain activity. Scientific reports, 9(1), 4927.
  68. Panzeri, S., & Piasini, E. (Eds.). (2019). Information Theory in Neuroscience. MDPI.
  69. Costa, A. C., Ahamed, T., & Stephens, G. J. (2019). Adaptive, locally linear models of complex dynamics. Proceedings of the National Academy of Sciences, 116(5), 1501-1510.
  70. Toker, D., & Sommer, F. T. (2019). Information integration in large brain networks. PLoS computational biology, 15(2), e1006807.
  71. Ma, L., Liu, W., & Hudson, A. E. (2019). Propofol Anesthesia Increases Long-range Frontoparietal Corticocortical Interaction in the Oculomotor Circuit in Macaque Monkeys. Anesthesiology: The Journal of the American Society of Anesthesiologists.
  72. Wang, Q., Valdes-Hernandez, P. A., Bosch-Bayard, J., Oosugi, N., Komatsu, M., Fujii, N., & Valdes-Sosa, P. A. (2018). EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons. bioRxiv, 350199.
  73. Chang, Y. J. (2018). Signal translation between EEG and ECoG to improve non-invasive based BCI performance (Doctoral dissertation).
  74. Muthukumaraswamy, S. D., & Liley, D. T. (2018). 1/f electrophysiological spectra in resting and drug-induced states can be explained by the dynamics of multiple oscillatory relaxation processes. NeuroImage, 179, 582-595.
  75. Bagyalakshmi, G., Rajkumar, G., Arunkumar, N., Easwaran, M., Narasimhan, K., Elamaran, V., ... & Ramirez-Gonzalez, G. (2018). Network vulnerability analysis on brain signal/image databases using Nmap and Wireshark tools. IEEE Access, 6, 57144-57151.
  76. Marrouch, N., Read, H. L., Slawinska, J., & Giannakis, D. (2018, July). Data-driven spectral decomposition of ECoG signal from an auditory oddball experiment in a marmoset monkey: Implications for EEG data in humans. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-10). IEEE.
  77. Halgren, M., Ulbert, I., Bastuji, H., Fabo, D., Eross, L., Rey, M., ... & Wittner, L. (2018). The generation and propagation of the human alpha rhythm. bioRxiv, 202564.
  78. Motrenko, A., & Strijov, V. (2018). Multi-way feature selection for ECoG-based Brain-Computer Interface. Expert Systems with Applications, 114, 402-413.
  79. Dimitriadis, S. I. (2018). Complexity of brain activity and connectivity in functional neuroimaging. Journal of neuroscience research, 96(11), 1741-1757.
  80. ISACHENKO, R., VLADIMIROVA, M., & STRIJOV, V. (2018). Dimensionality Reduction for Time Series Decoding and Forecasting Problems. DEStech Transactions on Computer Science and Engineering, (optim).
  81. Farrokhi, B., & Erfanian, A. (2018). A piecewise probabilistic regression model to decode hand movement trajectories from epidural and subdural ECoG signals. Journal of neural engineering, 15(3), 036020.
  82. Shimono, M., & Hatano, N. (2018). Efficient communication dynamics on macro-connectome, and the propagation speed. Scientific reports, 8(1), 2510.
  83. Kitazono, J., Kanai, R., & Oizumi, M. (2018). Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory. Entropy, 20(3), 173.
  84. Bola, M., Barrett, A. B., Pigorini, A., Nobili, L., Seth, A. K., & Marchewka, A. (2018). Loss of consciousness is related to hyper-correlated gamma-band activity in anesthetized macaques and sleeping humans. NeuroImage, 167, 130-142.
  85. Hou, M., & Chaib-draa, B. (2017, August). Fast recursive low-rank tensor learning for regression. In Internatiaonal Joint Conference on Artificial Intelligence (IJCAI) (pp. 1851-1857).
  86. Foodeh, R., Khorasani, A., Shalchyan, V., & Daliri, M. R. (2017). Minimum Noise Estimate filter: a Novel Automated Artifacts Removal method for Field Potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(8), 1143-1152.
  87. Eliseyev, A., Auboiroux, V., Costecalde, T., Langar, L., Charvet, G., Mestais, C., Aksenova, T., & Benabid, A. L. (2017). Recursive Exponentially Weighted N-way Partial Least Squares Regression with Recursive-Validation of Hyper-Parameters in Brain-Computer Interface Applications. Scientific reports, 7(1), 16281.
  88. Gao, R., Peterson, E. J., & Voytek, B. (2017). Inferring synaptic excitation/inhibition balance from field potentials. Neuroimage, 158, 70-78.
  89. Krzeminski, D., Kaminski, M., Marchewka, A., & Bola, M. (2017). Breakdown of long-range temporal correlations in brain oscillations during general anesthesia. NeuroImage, 159, 146-158.
  90. Schaeffer, M. C., & Aksenova, T. (2017). Switching Markov decoders for asynchronous trajectory reconstruction from ECoG signals in monkeys for BCI applications. Journal of Physiology-Paris.
  91. Moon, J. Y., Kim, J., Ko, T. W., Kim, M., Iturria-Medina, Y., Choi, J. H., ... & Lee, U. (2017). Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species. Scientific Reports, 7.
  92. Shimono, M., & Hatano, N. (2017). Communicability Systematically Explains Transmission Speed In A Cortical Macro-Connectome. bioRxiv, 117713.
  93. Engel, S., Aksenova, T., & Eliseyev, A. (2017, February). Kernel-Based NPLS for Continuous Trajectory Decoding from ECoG Data for BCI Applications. In International Conference on Latent Variable Analysis and Signal Separation(pp. 417-426). Springer, Cham.
  94. Wen, H., & Liu, Z. (2016). Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain topography, 29(1), 13-26.
  95. Schaeffer, M. C., & Aksenova, T. (2016, September). Hybrid Trajectory Decoding from ECoG Signals for Asynchronous BCIs. In International Conference on Artificial Neural Networks (pp. 288-296). Springer International Publishing.
  96. Padovani, E. C. (2016). Characterization of Large Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction. arXiv preprint arXiv:1604.00002.
  97. Foodeh, R., Khorasani, A., Shalchyan, V., & Daliri, M. R. (2016). Minimum Noise Estimate filter: a Novel Automated Artifacts Removal method for Field Potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering.
  98. Gao, R. D., Peterson, E. J., & Voytek, B. (2016). Inferring Synaptic Excitation/Inhibition Balance from Field Potentials. bioRxiv, 081125.
  99. Wen, H., & Liu, Z. (2016). Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal. Journal of Neuroscience, 36(22), 6030-6040.
  100. Oizumi, M., Amari, S. I., Yanagawa, T., Fujii, N., & Tsuchiya, N. (2016). Measuring integrated information from the decoding perspective. PLoS Comput Biol, 12(1), e1004654.
  101. Eliseyev, A., & Aksenova, T. (2016). Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording. PloS one, 11(5), e0154878.
  102. Hou, M., & Chaib-draa, B. (2016, March). Online incremental higher-order partial least squares regression for fast reconstruction of motion trajectories from tensor streams. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on (pp. 6205-6209). IEEE.
  103. Solovey, G., Alonso, L. M., Yanagawa, T., Fujii, N., Magnasco, M. O., Cecchi, G. A., & Proekt, A. (2015). Loss of consciousness is associated with stabilization of cortical activity. Journal of Neuroscience, 35(30), 10866-10877.
  104. Papadopoulou, M., Friston, K., & Marinazzo, D. (2015). Estimating directed connectivity from cortical recordings and reconstructed sources. Brain topography, 1-12.
  105. Hou, M., Wang, Y., & Chaib-draa, B. (2015, April). Online local gaussian process for tensor-variate regression: Application to fast reconstruction of limb movements from brain signal. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 5490-5494). IEEE.
  106. Kasai, K., Fukuda, M., Yahata, N., Morita, K., & Fujii, N. (2015). The future of real-world neuroscience: imaging techniques to assess active brains in social environments. Neuroscience research, 90, 65-71.
  107. Tajima, S., Yanagawa, T., Fujii, N., & Toyoizumi, T. (2015). Untangling brain-wide dynamics in consciousness by cross-embedding. PLoS Comput Biol, 11(11), e1004537.
  108. van Driel, J., Cox, R., & Cohen, M. X. (2015). Phase-clustering bias in phase–amplitude cross-frequency coupling and its removal. Journal of neuroscience methods, 254, 60-72.
  109. Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G., Zhao, Q., Caiafa, C., & Phan, H. A. (2015). Tensor decompositions for signal processing applications: From two-way to multiway component analysis. IEEE Signal Processing Magazine, 32(2), 145-163.
  110. Tajima S, Toyoizumi T (2014). "Understanding large-scale complex systems with embedding." Seitai no Kagaku 65(5): 478-479.
  111. Eliseyev A, Aksenova T (2014). "Stable and artifact-resistant decoding of 3D hand trajectories from ECoG signals using the generalized additive model." J. Neural Eng. 11(6): 066005.
  112. de Cheveigné A, Lucas CP (2014). "Joint decorrelation, a versatile tool for multichannel data analysis." NeuroImage 98:487-505.
  113. Keshtkaran MR, Yang Z (2014). "A fast, robust algorithm for power line interference cancellation in neural recording." J. Neural Eng. 11(2): 026017.
  114. Syed MN, Georgiev PG, Pardalos PM (2014). "Blind Signal Separation Methods in Computational Neuroscience." Neuromethods.
  115. Komatsu M, Namikawa J, Chao ZC, Nagasaka Y, Fujii N., Nakamura K, Tani J (2014). "An artificial network model for estimating the network structure underlying partially observed neuronal signals." Neuroscience research 81-82:69-77.
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