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.
|Journal of Neural Engineering
|Published - Feb 2019
Bibliographical noteFunding Information:
The authors are grateful to Dr Jose Millan for his insightful comments to an early draft of this manuscript. The assistance of Megan Pitz to the manuscript is also appreciated. This work was partially supported by NeuroNET at UTK.
© 2019 IOP Publishing Ltd.
- BCI paradigm
- brain-computer interface
ASJC Scopus subject areas
- Biomedical Engineering
- Cellular and Molecular Neuroscience