Abstract
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.
Highlights
Brain-computer interface (BCI) allows people to establish an alternative communication channel between the user’s intention and output devices which is completely independent of the normal motor output paths of the nervous system (Gandhi, 2007; Volosyak et al, 2017)
Among different types of VEPs, steadystate visual evoked potential (SSVEP) is a continuous electrical activity recorded at the occipital and parietal cortex areas, which is elicited at the same frequency when the retina is excited by visual stimuli at a specific frequency (Luo and Sullivan, 2010)
To improve user experience of VEP-BCIs, this study proposes to design and implement a hybrid BCI system based on the low-frequency (
Summary
Brain-computer interface (BCI) allows people to establish an alternative communication channel between the user’s intention and output devices which is completely independent of the normal motor output paths of the nervous system (Gandhi, 2007; Volosyak et al, 2017). Electroencephalography (EEG) is the most favorable method in non-invasive BCIs (Gao et al, 2014) due to its essential attributes such as low cost, high time resolution, and easy access to data (Mason et al, 2007; Volosyak, 2011). Many laboratories and clinical tests have demonstrated the convincing robustness of visual evoked potential (VEP)-based BCI systems (Wang et al, 2008). SSVEP has been recognized as a reliable, fast, and easy-to-use communication paradigm (Allison et al, 2010) due to its high information transfer rate (ITR), little training cost, and fewer electrode requirements (Hoffmann et al, 2009; Brunner et al, 2010)
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