This study proposed a dynamic model which was helpful to understand and utilize the transient characteristics of SSVEP. This study also found that pre-stimulation could be used to adjust the parameters of SSVEP model, and had the potential to improve the performance of SSVEP-BCI.

This paper reports on a benchmark dataset acquired with a brain–computer interface (BCI) system based on the rapid serial visual presentation (RSVP) paradigm. The dataset consists of 64-channel electroencephalogram (EEG) data from 64 healthy subjects (sub1,…, sub64) while they performed a target image detection task. For each subject, the data contained two groups (“A” and “B”). Each group contained two blocks, and each block included 40 trials that corresponded to 40 stimulus sequences. Each sequence contained 100 images presented at 10 Hz (10 images per second). The stimulus images were street-view images of two categories: target images with human and non-target images without human. Target images were presented randomly in the stimulus sequence with a probability of 1∼4%. During the stimulus presentation, subjects were asked to search for the target images and ignore the non-target images in a subjective manner. To keep all original information, the dataset was the raw continuous data without any processing. On one hand, the dataset can be used as a benchmark dataset to compare the algorithms for target identification in RSVP-based BCIs. On the other hand, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data through offline simulation. Furthermore, the dataset also provides high-quality data for characterizing and modeling event-related potentials (ERPs) and steady-state visual evoked potentials (SSVEPs) in RSVP-based BCIs. The dataset is freely available from http://bci.med.tsinghua.edu.cn/download.html .

Objective. A visual stimulator plays a vital part in brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP). The properties of visual stimulation, such as frequency, color, and waveform, will influence SSVEP-based BCI performance to some extent. Recently, the computer monitor serves as a visual stimulator that is widespread in SSVEP-based BCIs because of its great flexibility in generating visual stimuli. However, stimulation properties based on a computer monitor have received very little attention. For a better comprehension of SSVEPs, this study explored the stimulation effects of waveforms and frequencies, when evoking SSVEPs through a computer monitor. Approach. This study utilized the approximation methods to realize sine-and square-wave temporal modulations at 18 stimulation frequencies ranging from 6 to 40 Hz on a conventional 120 Hz LCD screen. We collected electroencephalogram (EEG) datasets from 12 healthy subjects and compared the signal-to-noise ratios (SNRs), amplitudes, and topographic mapping of SSVEPs evoked by these two temporal modulation flickers (sine-and square-wave). In addition, a BCI experiment with two nine-target BCIs (i.e. low-frequency BCI and high-frequency BCI) was implemented to compare the two stimulation waveforms in terms of BCI performance. Main results. For both sine-and square-wave stimulation conditions, strong SSVEPs over the occipital area were observed for each stimulation frequency. SSVEP amplitudes at the stimulation frequency exhibited a global peak in the low-frequency band. The second harmonic SSVEP frequency-response functions showed the largest amplitude at 6 Hz and fell sharply for higher frequencies. In the BCI experiment, the classification performance of the squarewave stimuli was notably higher than that of the sine-wave stimuli when using shorter data lengths. Significance. These results suggested that the square-wave flicker was more efficient at implementing high-speed BCIs based on SSVEP when using a computer monitor as a visual stimulator.

Objective. Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) has been widely investigated because of its easy system configuration, high information transfer rate (ITR) and little user training. However, due to the limitations of brain responses and the refresh rate of a monitor, the available stimulation frequencies for practical BCI application are generally restricted. Approach. This study introduced a novel stimulation method using intermodulation frequencies for SSVEP-BCIs that had targets flickering at the same frequency but with different additional modulation frequencies. The additional modulation frequencies were generated on the basis of choosing desired flickering frequencies. The conventional frame-based ‘on/off’ stimulation method was used to realize the desired flickering frequencies. All visual stimulation was present on a conventional LCD screen. A 9-target SSVEP-BCI based on intermodulation frequencies was implemented for performance evaluation. To optimize the stimulation design, three approaches (C: chromatic; L: luminance; CL: chromatic and luminance) were evaluated by online testing and offline analysis. Main results. SSVEP-BCIs with different paradigms (C, L, and CL) enabled us not only to encode more targets, but also to reliably evoke intermodulation frequencies. The online accuracies for the three paradigms were 91.67% (C), 93.98% (L), and 96.41% (CL). The CL condition achieved the highest classification performance. Significance. These results demonstrated the efficacy of three approaches (C, L, and CL) for eliciting intermodulation frequencies for multi-class SSVEP-BCIs. The combination of chromatic and luminance characteristics of the visual stimuli is the most efficient way for the intermodulation frequency coding method.

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