TY - JOUR
T1 - Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface
T2 - A Review
AU - Zhang, Yue
AU - Xie, Sheng Quan
AU - Wang, He
AU - Zhang, Zhiqiang
PY - 2021/1/15
Y1 - 2021/1/15
N2 - Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this article, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this article. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.
AB - Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this article, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this article. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.
UR - http://dx.doi.org/10.1109/jsen.2020.3017491
UR - https://www.scopus.com/pages/publications/85098212299
U2 - 10.1109/jsen.2020.3017491
DO - 10.1109/jsen.2020.3017491
M3 - Article
SN - 1530-437X
VL - 21
SP - 1124
EP - 1138
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 2
ER -