[Original researches of data] 
  1. Komatsu, M., Sugano, E., Tomita, H., & Fujii, N. (2017). A chronically implantable bidirectional neural interface for non-human primates. Frontiers in Neuroscience, 11:514.
  2. 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.
  3. 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.
  4. Chao, Z. C., Nagasaka, Y., & Fujii, N. (2015). Cortical network architecture for context processing in primate brain. eLife, 4, e06121.
  5. Komatsu, M., Takaura, K., & Fujii, N. (2015). Mismatch negativity in common marmosets: Whole-cortical recordings with multi-channel electrocorticograms. Scientific reports, 5, 15006.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.

  1. Papadopoulou, M., Friston, K., & Marinazzo, D. (2019). Estimating directed connectivity from cortical recordings and reconstructed sources. Brain topography, 32(4), 741-752.
  2. 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.
  3. 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.
  4. Agarwal, N., Kathpalia, A., & Nagaraj, N. (2019). Distinguishing Different Levels Of Consciousness using a Novel Network Causal Activity Measure. bioRxiv, 660043.
  5. 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.
  6. Canales-Johnson, A., Borges, A. F. T., Komatsu, M., Fujii, N., Fahrenfort, J. J., Miller, K. J., & Noreika, V. (2019). Broadband Signal Rather than Frequency-Specific Rhythms Underlies Prediction Error in the Primate Auditory Cortex. bioRxiv, 821942.
  7. 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.
  8. Harris, K. D., Aravkin, A., Rao, R., & Brunton, B. W. (2019). Time-varying Autoregression with Low Rank Tensors. arXiv preprint arXiv:1905.08389.
  9. 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.
  10. Panzeri, S., & Piasini, E. (Eds.). (2019). Information Theory in Neuroscience. MDPI.
  11. 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.
  12. Toker, D., & Sommer, F. T. (2019). Information integration in large brain networks. PLoS computational biology, 15(2), e1006807.
  13. 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.
  14. 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.
  15. Chang, Y. J. (2018). Signal translation between EEG and ECoG to improve non-invasive based BCI performance (Doctoral dissertation).
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. Motrenko, A., & Strijov, V. (2018). Multi-way feature selection for ECoG-based Brain-Computer Interface. Expert Systems with Applications, 114, 402-413.
  21. Dimitriadis, S. I. (2018). Complexity of brain activity and connectivity in functional neuroimaging. Journal of neuroscience research, 96(11), 1741-1757.
  22. ISACHENKO, R., VLADIMIROVA, M., & STRIJOV, V. (2018). Dimensionality Reduction for Time Series Decoding and Forecasting Problems. DEStech Transactions on Computer Science and Engineering, (optim).
  23. 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.
  24. Shimono, M., & Hatano, N. (2018). Efficient communication dynamics on macro-connectome, and the propagation speed. Scientific reports, 8(1), 2510.
  25. Kitazono, J., Kanai, R., & Oizumi, M. (2018). Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory. Entropy, 20(3), 173.
  26. 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.
  27. 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).
  28. 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.
  29. 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.
  30. Gao, R., Peterson, E. J., & Voytek, B. (2017). Inferring synaptic excitation/inhibition balance from field potentials. Neuroimage, 158, 70-78.
  31. 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.
  32. 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.
  33. 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.
  34. Shimono, M., & Hatano, N. (2017). Communicability Systematically Explains Transmission Speed In A Cortical Macro-Connectome. bioRxiv, 117713.
  35. 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.
  36. Wen, H., & Liu, Z. (2016). Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain topography, 29(1), 13-26.
  37. 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.
  38. Padovani, E. C. (2016). Characterization of Large Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction. arXiv preprint arXiv:1604.00002.
  39. 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.
  40. Gao, R. D., Peterson, E. J., & Voytek, B. (2016). Inferring Synaptic Excitation/Inhibition Balance from Field Potentials. bioRxiv, 081125.
  41. Wen, H., & Liu, Z. (2016). Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal. Journal of Neuroscience, 36(22), 6030-6040.
  42. 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.
  43. Eliseyev, A., & Aksenova, T. (2016). Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording. PloS one, 11(5), e0154878.
  44. 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.
  45. 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.
  46. Papadopoulou, M., Friston, K., & Marinazzo, D. (2015). Estimating directed connectivity from cortical recordings and reconstructed sources. Brain topography, 1-12.
  47. 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.
  48. 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.
  49. Tajima, S., Yanagawa, T., Fujii, N., & Toyoizumi, T. (2015). Untangling brain-wide dynamics in consciousness by cross-embedding. PLoS Comput Biol, 11(11), e1004537.
  50. 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.
  51. 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.
  52. Tajima S, Toyoizumi T (2014). "Understanding large-scale complex systems with embedding." Seitai no Kagaku 65(5): 478-479.
  53. 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.
  54. de Cheveigné A, Lucas CP (2014). "Joint decorrelation, a versatile tool for multichannel data analysis." NeuroImage 98:487-505.
  55. Keshtkaran MR, Yang Z (2014). "A fast, robust algorithm for power line interference cancellation in neural recording." J. Neural Eng. 11(2): 026017.
  56. Syed MN, Georgiev PG, Pardalos PM (2014). "Blind Signal Separation Methods in Computational Neuroscience." Neuromethods.
  57. 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.
  58. Zhao Q, Zhang L, Cichocki A (2014). "Multilinear and nonlinear generalizations of partial least squares: an overview of recent advances." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4(2): 104-115.
  59. Zhao Q, Zhou G, Adali T, Zhang L, Cichocki A (2013). "Kernel-based tensor partial least squares for reconstruction of limb movements." IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3577-3581.
  60. Zhao Q, Zhou G, Adali T, Zhang L, Cichocki A (2013) "Kernelization of Tensor-Based Models for Multiway Data Analysis: Processing of Multidimensional Structured Data." IEEE Signal Processing Magazine 30(4): 137-148.
  61. Eliseyev A, Aksenova T (2013). "Recursive N-Way Partial Least Squares for Brain-Computer Interface." PLOS ONE 8(7): e69962.
  62. Vakorin VA, MišiāEB, Krakovska O, Bezgin G, McIntosh AR (2013). "Confounding Effects of Phase Delays on Causality Estimation." PLOS ONE 8(1): e53588.
  63. van Gerven MAJ, Chao ZC, Heskes T (2012). "On the decoding of intracranial data using sparse orthonormalized partial least squares." J. Neural Eng. 9, 026017.
  64. Zhao Q, Caiafa CF, Mandic DP, Chao ZC, Nagasaka Y, Fujii N, Zhang L, Cichocki A (2012). "Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method." IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 35:7, 1660-1673.
  65. Komatsu M, Namikawa J, Tani J, Chao ZC, Nagasaka Y, Fujii N, Nakamura K (2012). "Estimation of functional brain connectivity from electrocorticograms using an artificial network model." IEEE International Joint Conference on Neural Networks (IJCNN), p1-8.
  66. Schwabe L, Zheng Y (2012). "Towards Model-Based Brain Imaging with Multi-Scale Modeling." Neuroimaging - Methods. P. Bright(editor), InTech: 99-114.
  67. Ozkurt TE (2012). "Statistically Reliable and Fast Direct Estimation of Phase-Amplitude Cross-Frequency Coupling." IEEE Transactions on Biomedical Engineering, 59(7): 1943-1950.