Title :
A new online event related potential based brain-computer interfaces using an ensemble classifier
Author :
Onishi, Akinari ; Natsume, K.
Author_Institution :
Dept. of Brain Sci. & Eng., Kyushu Inst. of Technol., Kitakyushu, Japan
Abstract :
The brain computer interface (BCI) records brain signals, then translates them into commands for control devices for a better quality of life. The event related potential (ERP) has been used to drive the BCIs, but they still have room for improving its performance toward a practical use. In this study we evaluated an online ERP-based BCI system that had an ensemble classifier with overlapped partitioning and a stimulator which provided image intensifications. We also employed and compared four types of visual stimuli: a neutral, a positive, a negative and a traditional gray/white letter stimulus. As a result, ERP-based BCI system with the neutral, the positive and the negative image achieved significantly higher information transfer rate (41.0 ± 2.2 bits per minute at best) than that with the conventional gray/white letter stimulus.
Keywords :
brain-computer interfaces; learning (artificial intelligence); medical signal processing; signal classification; visual evoked potentials; brain signals; brain-computer interfaces; ensemble classifier; event related potential; image intensifications; negative stimulus; neutral stimulus; online ERP-based BCI system; overlapped partitioning; positive stimulus; quality of life; traditional gray-white letter stimulus; visual stimuli; Accuracy; Brain-computer interfaces; Databases; Principal component analysis; Training; Training data; Vectors;
Conference_Titel :
Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on
Conference_Location :
San Diego, CA
DOI :
10.1109/NER.2013.6696113
