期刊
ELECTRONICS
卷 11, 期 24, 页码 -出版社
MDPI
DOI: 10.3390/electronics11244231
关键词
brain-computer interfaces; filter bank canonical correlation analysis; information transfer rate; steady-state visual-evoked potential
资金
- National Natural Science Foundation of China
- [61802425]
- [62171466]
- [62001515]
The rapid development of brain-computer interfaces (BCIs) has been observed in the past decade. The contradiction between communication rates and tedious training processes is one of the major barriers in the application of SSVEP-based BCIs. This study proposes a turbo detector that uses FBCCA as the first-stage detector and utilizes soft information and identified data pool to improve performance.
The past decade has witnessed the rapid development of brain-computer interfaces (BCIs). The contradiction between communication rates and tedious training processes has become one of the major barriers restricting the application of steady-state visual-evoked potential (SSVEP)-based BCIs. A turbo detector was proposed in this study to resolve this issue. The turbo detector uses the filter bank canonical correlation analysis (FBCCA) as the first-stage detector and then utilizes the soft information generated by the first-stage detector and the pool of identified data generated during use to complete the second-stage detection. This strategy allows for rapid performance improvements as the data pool size increases. A standard benchmark dataset was used to evaluate the performance of the proposed method. The results show that the turbo detector can achieve an average ITR of 130 bits/min, which is about 8% higher than FBCCA. As the size of the data pool increases, the ITR of the turbo detector could be further improved.
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