Zhang, Yue;
Xie, Shane Q;
Wang, He;
Zhang, Zhiqiang;
(2021)
Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review.
IEEE Sensors Journal
, 21
(2)
pp. 1124-1138.
10.1109/JSEN.2020.3017491.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JSEN.2020.3017491 |
| Publisher version: | https://doi.org/10.1109/jsen.2020.3017491 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Brain-computer interface (BCI), canonical correlation analysis, CANONICAL CORRELATION-ANALYSIS, CLASSIFICATION, COMPONENT ANALYSIS, Correlation, Data analysis, data analytics, Electroencephalography, EMPIRICAL MODE DECOMPOSITION, Engineering, Engineering, Electrical & Electronic, ENHANCING DETECTION, FEATURE-EXTRACTION, FREQUENCY-DOMAIN, healthcare application, Instruments & Instrumentation, Medical services, NEAR-INFRARED SPECTROSCOPY, PHASE, Physical Sciences, Physics, Physics, Applied, Science & Technology, Sensors, SSVEP-BASED BCI, steady state visual evoked potential (SSVEP), Steady-state, Technology, Visualization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215241 |
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