TY  - CHAP
AU  - S. Shigezumi
AU  - H. Hara
AU  - H. Namba
AU  - C. Serizawa
AU  - Y. Dobashi
AU  - A.
Takemoto
AU  - K. Nakamura
AU  - T. Matsumoto
ED  - Reza Fazel-Rezai
Y1  - 2013-06-05
PY  - 2013
T1  - Bayesian Sequential Learning for EEG-Based BCI Classification Problems
N2  - Brain-Computer Interface (BCI) systems allow communication based on a direct electronic interface which conveys messages and commands directly from the human brain to a computer. In the recent years, attention to this new area of research and the number of publications discussing different paradigms, methods, signal processing algorithms, and applications have been increased dramatically. The objective of this book is to discuss recent progress and future prospects of BCI systems. The topics discussed in this book are: important issues concerning end-users; approaches to interconnect a BCI system with one or more applications; several advanced signal processing methods (i.e., adaptive network fuzzy inference systems, Bayesian sequential learning, fractal features and neural networks, autoregressive models of wavelet bases, hidden Markov models, equivalent current dipole source localization, and independent component analysis); review of hybrid and wireless techniques used in BCI systems; and applications of BCI systems in epilepsy treatment and emotion detections.
BT  - Brain-Computer Interface Systems
SP  - Ch. 4
UR  - https://doi.org/10.5772/56146
DO  - 10.5772/56146
SN  - 
PB  - IntechOpen
CY  - Rijeka
Y2  - 2024-04-27
ER  -