脑-机接口
神经康复
脑电图
计算机科学
运动表象
接口(物质)
神经反射
人机交互
人工智能
神经科学
康复
心理学
最大气泡压力法
气泡
并行计算
作者
Reza Abiri,Soheil Borhani,Eric W. Sellers,Yang Jiang,Xiaopeng Zhao
出处
期刊:Journal of Neural Engineering
[IOP Publishing]
日期:2019-01-09
卷期号:16 (1): 011001-011001
被引量:627
标识
DOI:10.1088/1741-2552/aaf12e
摘要
Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.
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