# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
## 1 Introduction  
As graphical user interface (GUI) technology is nearing its 50 th anniversary, humans are actively seeking new modes of interaction with machines. A user, considered as a source of information, can use their gestures, voice, eye movements, etc., to generate control commands or actions. A promising communication channel is the one based on the neural activity of the brain (hence, the so-called brain-computer interface (BCI)). Its important advantage is accessibility, even for people whose motor reactions are disrupted or cease completely. For healthy people, BCIs hold a compelling promise to \"read one's mind\", especially when in conjunction with booming artificial intelligence (AI) technologies [1]. Although BCI technology is not yet fully mature, it has been applied to control a wide variety of devices, including computers, motorized wheelchairs [2], and exoskeletons. It is used to monitor cognitive states, mental loads, and fatigue [3], as well as in rehabilitation systems for stroke patients [4]. There are many paradigms and approaches on the basis of which BCIs are built and function [5]. The one that demonstrates the highest interaction accuracy and speed [6] is steady-state visually evoked potential (SSVEP), which involves a specific neuronal response that occurs as a result of presenting a periodic visual stimulus to a subject [7]. This reaction is mainly localized to the occipital region of the cerebral cortex and can be recorded in the O1, O2, and Oz leads placed according to the \"10-20\" system for electroencephalography (EEG) [8]. The main characteristics of these potentials are their frequencies, which strictly depend on the frequencies of visual stimuli, and power, which is usually significantly higher than the power of the baseline brainwave activity. The widely claimed advantages of SSVEP-based BCIs include the following [9]:The high speed of the information transfer;  
The minimal time required for user training and relative ease of installation;  
The safety for the users. The aforementioned immaturity of the BCI technology leaves much to be desired for each of the above points. First, much of the current research in the field is focused on the light stimuli of high frequencies (for EEG signals, this means more than 30 Hz [10]) to enhance the _information transfer rate_ (ITR) or effective bit rate, as introduced in [11], and reported on in BCI studies. ITR is a common metric in SSVEP-BCI interfaces, aiding in the estimation and comparison of different identification algorithms by combining speed and accuracy. Indeed, the low-frequency range of EEG signals is affected by high-amplitude artifacts, such as EOG artifacts (concentrated in the frequency range of 1 to 5 Hz [12]). Moreover, it is overlapped by a more powerful alpha-rhythm of the EEG signal, concentrated in the frequency range of 8 to 14 Hz. However, SSVEP potentials are usually most intense in the frequency range of around 15 Hz, and the power of the evoked potential decreases with the increase of the light stimulus frequency [13]. Second, BCIs are rather less universal than most other human-machine interface modes, as the former generally require calibration and adaptation to the user's individual characteristics. SSVEP-based BCIs have a wide scope of applications, ranging from controlling smart home devices [14] to enabling communication with patients with disorders of consciousness and facilitating the rehabilitation of individuals after severe head and spinal cord injuries [15; 16]. In these scenarios, the usability and minimal training duration required to ensure reliable operation take precedence. Indeed, as mentioned above, in some people, the response level to a certain stimulus frequency may be too low relative to the EEG signal itself, making this frequency unsuitable for use [10]. This makes it difficult to build a universal BCI that uses a single frequency range for its users.