A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and "BCI illiteracy...

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Bibliographic Details
Published in:Frontiers in human neuroscience 2022-02, Vol.16, p.834959-834959
Main Authors: Jiang, Lu, Li, Xiaoyang, Pei, Weihua, Gao, Xiaorong, Wang, Yijun
Format: Article
Language:English
Subjects:
Accuracy
BCI illiteracy
Brain
Brain research
Classification
Communication
Computer applications
Correlation analysis
electroencephalogram
Electroencephalography
Experiments
Human-computer interaction
hybrid brain-computer interface
Implants
Neuroscience
pupillary response
task-related component analysis
User experience
visual evoked potential
Visual evoked potentials
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Summary:Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and "BCI illiteracy." To address these problems, this study proposes to use low-frequency stimulations (12 classes, 0.8-2.12 Hz with an interval of 0.12 Hz), which can simultaneously elicit visual evoked potential (VEP) and pupillary response (PR) to construct a hybrid BCI (h-BCI) system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% (data length 1.5 s) for the supervised method and 91.88 ± 3.68% (data length 4 s) for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min (bpm) and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects' feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range.
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2022.834959