| 时间 |
2022年10月11日，12:00-13:00 |
| 地点 |
生物医学馆E303 |
| 主持人 |
|
| 报告人 |
刘冰川 |
| 题目 | Steady-State Visual Evoked Potential Based Brain-Computer Interface: Novel Design, Dataset and Decoding (English) |
| 摘要 | In the past half-century, brain-computer interface (BCI) technology has witnessed significant progress and has been undergoing a flourish in this field. Among the noninvasive BCI paradigms, steady-state visual evoked potential based BCI (SSVEP-BCI) has become one of the main paradigms and has received widespread attention due to its outstanding advantages such as high information transfer rate and low BCI illiterate rate. However, the system performance of SSVEP-BCI is still relatively low compared with conventional communication methods. Several problems in SSVEP-BCI, such as improving the performance in encoding and decoding, and reducing the calibration time, still need to be addressed. To address these issues, this talk introduces several studies, including the design of the SSVEP-BCI system with a large number of stimuli, curation of large-scale datasets, and a series of BCI decoding research based on model-driven algorithms and data-driven algorithms, further pushing the boundary of SSVEP-BCI toward real-world applications. |
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