# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
## 1 Introduction
The need for calibration is recognized as a major obstacle for the wider BCI development and hurts the overall user experience (UX) [17; 18]. The currently popular approach to the so-called _subject calibration problem_ is \"subject-transfer\" [19], which largely corresponds to the general transfer learning approaches in AI-ML: instance-based, parameter-based, or feature-based [20]. Correspondingly, methods and models that improve the trade-off between the calibration effort and the BCI performance move the field forward, but this issue is far from being resolved yet. Third, the claimed safety of BCIs is rather situational. Even if we do not consider brain-invasive techniques, the photo stimuli can cause strong fatigue (particularly to the eyes) and even provoke an epileptic seizure for individuals suffering from photosensitive epilepsy [21]. Research suggests that these risks are frequency-specific, being more prone to the middle-frequency range of EEG signals (from 12 to 30 Hz) [10], and user group-specific [22]. [PERSON] and colleagues demonstrated in their recent study that SSVEP target-classification accuracy decreases under the influence of stress [23]. To assess the extent of the impact of periodic photostimulation on the subjective emotional state of participants and to objectify the degree of discomfort experienced during interaction with neurointerfaces, researchers in this field employ indices that are specifically derived from the power ratios of major EEG frequency bands. For instance, theta-beta ratio [24], theta-alpha ratio [25], and frontal alpha asymmetry [26] have frequently been used as such indices in scientific literature. Incorporating these index values into the development of an algorithm for individually tailored stimulation frequencies could represent a crucial step toward creating more personalized and user-friendly interfaces. As any interfacing technology matures, considerations of _usefulness_ are supplemented by those of _usability_. BCI is no exception in this regard. About 10 years ago, an editorial in a dedicated Special Issue of _Ergonomics_ noted that \"for people with motor disabilities, the ease and convenience of use \(\ldots\) may be as important as speed of communication\" [27]. Indeed, calls for considering UX in BCI began over a decade ago [28; 29]. Currently, there is a growing interest in BCI usability evaluations, encompassing both objective and subjective measures. Clearly, this implies the need to extend the three points discussed above. A transition from a user interface (UI) to UX requires, at the very least, adding _user satisfaction_ to the puzzle. The main research question of our study can be formulated as follows: _Is it possible to determine, based on the recorded EEG signal, whether a given photosynthesis frequency is optimal in a given range for a particular person?_ Regarding the selection of the optimal stimulation frequency for the stable reproduction of the maximum SSVEP amplitude, opinions among researchers in the scientific community vary significantly. Broad ranges for observing SSVEP cover frequencies of 8.5-15 Hz [30], 12-18 Hz [31], or 12-30 Hz [32]. The wide variety of results obtained reflect the individual characteristics of the brain's background state, influencing subsequent functional activity, not only of the visual system but also the outcomes of the default mode network's interaction with other neural systems [33], making the aforementioned aim of our current study relevant. Solving this problem and improving the selection of individual photostimulus frequencies can make BCI usage more effective and satisfying for the users, by (1) increasing the information transfer rate (ITR), which in the case of BCIs is highly dependent on the calibration process, and (2) significantly improving the user experience by using stimulation frequencies that do not cause excessive fatigue or stress in the users. This is especially relevant for people who experience visual fatigue when using this type of BCI.