Steady state visual evoked potentials (SSVEPs) are steady state oscillatory potentials elicited in the electroencephalogram (EEG) by flicker stimulation. The frequency of these responses maches the frequency of the stimulation and of its harmonics and subharmonics. In this study, we investigated the origin of the harmonic and subharmonic components of SSVEPs, which are not well understood. We applied both sine and square wave visual stimulation at 5 and 15 Hz to human subjects and analyzed the properties of the fundamental responses and harmonically related components. In order to interpret the results, we used the well-established neural mass model that consists of interacting populations of excitatory and inhibitory cortical neurons. In our study, this model provided a simple explanation for the origin of SSVEP spectra, and showed that their harmonic and subharmonic components are a natural consequence of the nonlinear properties of neuronal populations and the resonant properties of the modeled network. The model also predicted multiples of subharmonic responses, which were subsequently confirmed using experimental data.

This article concerns one of the most important problems of brain-computer interfaces (BCI) based on Steady State Visual Evoked Potentials (SSVEP), that is the selection of the a-priori most suitable frequencies for stimulation. Previous works related to this problem were done either with measuring systems that have little in common with actual BCI systems (e.g., single flashing LED) or were presented on a small number of subjects, or the tested frequency range did not cover a broad spectrum. Their results indicate a strong SSVEP response around 10 Hz, in the range 13–25 Hz, and at high frequencies in the band of 40–60 Hz. In the case of BCI interfaces, stimulation with frequencies from various ranges are used. The frequencies are often adapted for each user separately. The selection of these frequencies, however, was not yet justified in quantitative group-level study with proper statistical account for inter-subject variability. The aim of this study is to determine the SSVEP response curve, that is, the magnitude of the evoked signal as a function of frequency. The SSVEP response was induced in conditions as close as possible to the actual BCI system, using a wide range of frequencies (5–30 Hz, in step of 1 Hz). The data were obtained for 10 subjects. SSVEP curves for individual subjects and the population curve was determined. Statistical analysis were conducted both on the level of individual subjects and for the group. The main result of the study is the identification of the optimal range of frequencies, which is 12–18 Hz, for the registration of SSVEP phenomena. The applied criterion of optimality was: to find the largest contiguous range of frequencies yielding the strong and constant-level SSVEP response.

Efforts to construct an effective brain-computer interface (BCI) system based on Steady State Visual Evoked Potentials (SSVEP) commonly focus on sophisticated mathematical methods for data analysis. The role of different stimulus features in evoking strong SSVEP is less often considered and the knowledge on the optimal stimulus properties is still fragmentary. The goal of this study was to provide insight into the influence of stimulus characteristics on the magnitude of SSVEP response. Five stimuli parameters were tested: size, distance, colour, shape, and presence of a fixation point in the middle of each flickering field. The stimuli were presented on four squares on LCD screen, with each square highlighted by LEDs flickering with different frequencies. Brighter colours and larger dimensions of flickering fields resulted in a significantly stronger SSVEP response. The distance between stimulation fields and the presence or absence of the fixation point had no significant effect on the response. Contrary to a popular belief, these results suggest that absence of the fixation point does not reduce the magnitude of SSVEP response. However, some parameters of the stimuli such as colour and the size of the flickering field play an important role in evoking SSVEP response, which indicates that stimuli rendering is an important factor in building effective SSVEP based BCI systems.

Electroencephalographic responses to periodic stimulation are termed steady-state visual evoked potentials (SSVEP). Their characteristics in terms of amplitude, frequency and phase are commonly assumed to be stationary. In this work, we tested this assumption in 30 healthy participants submitted to 50 trials of 60[Formula: see text]s flicker stimulation at 15[Formula: see text]Hz frequency. We showed that the amplitude of the first and second harmonic frequency components of SSVEP signals were in general not stable over time. The power (squared amplitude) of the fundamental component was stationary only in 30% the subjects, while the power at the second harmonic frequency was stationary in 66.7% of the group. The phases of both SSVEP frequency components were more stable over time, but could exhibit small drifts. The observed temporal changes were heterogeneous across the subjects, implying that averaging results over participants should be performed carefully. These results may contribute to improved design and analysis of experiments employing prolonged visual stimulation. Our findings offer a novel characterization of the temporal changes of SSVEP that may help to identify their physiological basis.

Abstract. Brain-Computer Interface (BCI) allows for non-muscular communication with external world, which may be the only way of communication for patients in a locked-in state. This paper presents a complete software framework for BCI, a novel hardware solution for stimuli rendering in BCIs based on Steady State Visual Evoked Potentials (SSVEP), and a univariate algorithm for detection of SSVEP in the EEG time series.OpenBCI is a complete software framework for brain-computer interfaces.Owing to an open license and modular architecture, it allows for flexible implementations of different communication channels in the serial or parallel hybrid mode, minimization of costs and improvements of stability and efficiency. Complete software is freely available from http://openbci.pl.BCI Appliance is a hardware solution that allows for dynamic control of menus with stable generation of stimuli for the SSVEP paradigm. The novelty consists of a design, whereby the LCD screen is illuminated from behind using an array of LEDs.Design pioneers also proposed a new line of thought about the user-centered design of BCI systems: a simple box with one on/off button, minimum embedded software, wireless connections to domotic and EEG acquisition devices, and user-controlled mode switching in a hybrid BCI.