Abstract
Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.
Highlights
Brain computer interface (BCI) constructs a direct communication channel between the brain and external devices by coding and decoding brain activities [1,2]
Even for all 20 blocks, which lasted a total of 1.5 to2 h, there was no clear descending trend of classification accuracy (Figure 11c). These results suggest that a wearable state visual evoked potential (SSVEP)-based Brain-computer interfaces (BCIs) has stable and reliable performance, which can
These results suggest that a wearable SSVEP-based BCI has stable and reliable performance, which can provide a promising way to implement BCI-based communication and control
Summary
Brain computer interface (BCI) constructs a direct communication channel between the brain and external devices by coding and decoding brain activities (mainly physiological electrical signals) [1,2]. To facilitate the evaluation of the performance of algorithms, open datasets for SSVEP-based. BCIs have emerged in recent years [24,25,26]. High efficiency of these open datasets has benefited the studies in high-speed BCI spellers for researchers. To improve the practicality of SSVEP-based BCIs, a wearable BCI system is in great demand. In more complex environments, practical applications of wearable BCI systems face more challenges in data acquisition, data analysis, and user experience. As far as we know, a public dataset with a large number of subjects for a wearable SSVEP-based BCI is still missing
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