Fundamental Theory of Brain Decoding and its Applications toNon-verbal Information Retrieval
Project Leader
YOSHIDA Shinichi, Dr. Eng.
Associate Professor, Information Engineering Systems Course
1. Objective
This project is aimed at: development of brain decoding technique using functional magnetic resonanceimaging (fMRI) and electro encephalography (EEG). We have studied nonverbal multimedia information
retrieval, particularly content-based image retrieval using computational intelligence algorithms (See [1-3]).
For more two years the brain computer interface (BCI) based has been under way at KUT. The core idea
comes from brain decoding a novel technique in neuroscience (See [4, 5]). To improve brain decoding
performance is an urgent requirement for the realization of BCI-based nonverbal information retrieval.
Brain activity measurement techniques and algorithms for machine learning for brain decoding will be
developed in this project.
2. Project Outline
To that end, the project will consist of the following phases:(a) Establishment of preprocessing signals and extracting features of fMRI/EEG brain activity data.
(b) Creation, implementation and refinement of machine learning algorithms using supervised andunsupervised algorithms for brain decoding.
(c) Realization of a brain decoding methodology for nonverbal multimedia information retrieval using theabove techniques.

3. Expected Performance
In this project, the successful candidate would be expected to:(a) Understand the basic principles of neural activity in the human brain and brain measurement devices such
as fMRI and EEG, including the ability to determine appropriate parameters of fMRI and EEG.
(b) Read related international journal papers independently and discuss with senior researchers.
(c) Conduct brain activity measurement experiments.
(d) Understand the theory of machine learning algorithms.
(e) Publish outcomes of research results in international journal papers, and present them at international
conferences.
4. Required Skills and Knowledge
The successful candidate for this project will have the following knowledge and skills:(a) Mathematical theories related to machine learning and computational intelligence, e.g., kernel trick forsupport vector machine (SVM), regression (SVR), and clustering (SVC). Linear algebra for singular value
decomposition (SVD) and probability, statistics for Bayesian inference and particle filter (sequential
Monte Carlo filter or extended Karman filter) are also required.
(b) Programming skill to implement above algorithm using C/C++/Matlab. Programming skills using MPI(Message Passing Interface) on Linux clusters may highly improve result of research.
(c) Ability to communicate and discuss in English.

References[1] SUN, Liang; YOSHIDA, Shinichi; CHENG, Xiaochun; LIANG, Yanchun; “A Cooperative Particle
Swarm Optimizer with Statistical Variable Interdependence Learning,” Information Sciences, Vol. 186, No. 1,
pp.20-39, 2012[2] SUN, Liang; YOSHIDA, Shinichi; LIANG, Yanchun, “A Support Vector and K-Means Based HybridIntelligent Data Clustering Algorithm,” IEICE Transactions on Information and Systems, Vol. E94, No. 11,pp.2234-2243[3] SUN, Liang; GE, Hongwei; YOSHIDA, Shinichi; LIANG, Yanchun; TAN, Guozhen, “Support vector
description of clusters for content-based image annotation,” Pattern Recognition, available online 18 October
2013, to be printed in 2013[4] NISHIMOT, et al., “Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies,”
Current Biology, 21, pp.1641-1646, 2011[5] KAMITANI, et al., “Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies,”
Nature Neuroscience, 8(5), pp.679-685, 2005

See our admission guidelines:
https://www.kochi-tech.ac.jp/english/admission/ssp_aft19oct/ssp_application_guideline.html
Contact
E-mail: yoshida.shinichi@kochi-tech.ac.jp