Research

Research Interests

My interest is to unravel nonlinear dynamics of brain computing, at the micro-macro scale. It means, to describe the brain (neurons, synapses, networks) by a nonlinear dynamical system, so that we can simulate & predict brain’s activity (EEG etc.) on a computer.

Another interest is how we can keep our mental states always healthier, with the aid of Computer Science. This may be achieved by focusing on daily social data.

Research Topics

  • Data-Driven Analysis of Nonlinear Human Brain Electrodynamics
    • T. Sase & M. Othman (2022). Prediction of ADHD from a Small Dataset Using an Adaptive EEG Theta/Beta Ratio and PCA Feature Extraction. Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. DOI
    • T. Sase & K. Kitajo (2021). The metastable brain associated with autistic-like traits of typically developing individuals. PLOS Comput Biol. 17(4): e1008929. DOI
    • T. Sase, J. P. Ramírez, K. Kitajo, K. Aihara, and Y. Hirata (2016). Estimating the level of dynamical noise in time series by using fractal dimensions. Phys Lett A 380(11–12): 1151–1163. DOI
  • Mathematical Modelling of Nonlinear Brain Electrodynamics
    • T. Sase & K. Kitajo (2021). The metastable brain associated with autistic-like traits of typically developing individuals. PLOS Comput Biol. 17(4): e1008929. DOI
    • T. Sase & R. Hassan (2019). Brain and Artificial Intelligence: From the Viewpoint of Spontaneous and Task-Evoked Brain Dynamics. J Comput Theor Nanosci. 16(3): 1081–1092. DOI
    • T. Sase, Y. Katori, M. Komuro, and K. Aihara (2017). Bifurcation Analysis on Phase-Amplitude Cross-Frequency Coupling in Neural Networks with Dynamic Synapses. Front Comput Neurosci. 11(18). DOI

Disciplines

Computational Neuroscience, Applied Mathematics

Expertise

dynamical systems, time series analysis, bifurcation analysis, signal processing, EEG

Programming Languages

C, C++, Java, Python, MATLAB