Current trends in Graz Brain-Computer Interface (BCI) research

被引：332

作者：

Pfurtscheller, G ^{[1
]}

Neuper, C ^{[1
]}

Guger, C ^{[1
]}

Harkam, W ^{[1
]}

Ramoser, H ^{[1
]}

Schlögl, A ^{[1
]}

Obermaier, B ^{[1
]}

Pregenzer, M ^{[1
]}

机构：

[1] Graz Univ Technol, Ludwig Boltzmann Inst Med Informat & Neuroinforma, Inst Biomed Engn, Dept Med Informat, A-8010 Graz, Austria

来源：

IEEE TRANSACTIONS ON REHABILITATION ENGINEERING | 2000年 / 8卷 / 02期

基金：

奥地利科学基金会;

关键词：

adaptive autoregressive models; brain-computer interface (BCI); common spatial patterns (CSP); electroencephalogram (EEG) feedback; event-related desynchronization (ERD);

D O I：

10.1109/86.847821

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns. EEG signals recorded from sensorimotor areas during mental imagination of specific movements are classified on-line and used e.g. for cursor control. In a number of on-line experiments, various methods for EEG feature extraction and classification have been evaluated.

引用

页码：216 / 219

页数：4

共 21 条

[1]

Bishop C. M., 1995, NEURAL NETWORKS PATT

[2] CLASSIFICATION OF NON-AVERAGED EEG DATA BY LEARNING VECTOR QUANTIZATION AND THE INFLUENCE OF SIGNAL PREPROCESSING [J].