A5100629706- Neural dynamics and brain function
- EEG and Brain-Computer Interfaces
- Blind Source Separation Techniques
- Neural and Behavioral Psychology Studies
Nagaoka University of Technology
2013-2018
Brain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends appropriate selection classifier parameters features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portable more compact BMI for practical applications, it is also desirable use system capable using information as few EEG channels possible. In present work, we propose method classifying P300 ERPs combination...
Recently, a brain-computer interface (BCI) using virtual sound sources has been proposed for estimating user intention via electroencephalogram (EEG) in an oddball task. However, its performance is still insufficient practical use. In this study, we examine the impact that shortening stimulus onset asynchrony (SOA) on auditory BCI. While very short SOA might improve performance, perception and task become difficult, event-related potentials (ERPs) may not be induced if too short. Therefore,...
The accurate detection of event-related potentials (ERPs) is great importance to construct brain-machine interfaces (BMI) and constitutes a classification problem in which the appropriate selection features from dense-array EEG signals tuning classifier parameters are critical. In present work, we propose method for classifying single-trial ERPs using combination Lifting Wavelet Transform (LWT), Support Vector Machines (SVM) Particle Swarm Optimization (PSO). particular, LWT filters, set...