An Adaptive SSVEP-Based Brain-Computer Interface to Compensate Fatigue-Induced Decline of Performance in Practical Application.

Brain-computer interfaces based on steady-state visual evoked potentials are promising communication systems for people with speech and motor disabilities. However, reliable SSVEP response requires user's attention, which degrades over time due to significant eye-fatigue when low-frequency visual stimuli (5-15 Hz) are used. Previous studies have shown that eye-fatigue can be reduced using high-frequency flickering stimuli (>25 Hz). Here, it is quantitatively demonstrated that the performance of a high-frequency SSVEP BCI decreases over time, but this amount of decrease can be compensated effectively by using two proposed adaptive algorithms. This leaded to a robust alternative communication system for practical applications. The asynchronous spelling system implemented in this study uses a threshold-based version of LASSO algorithm for frequency recognition. In long online experiments, when participants typed a sentence with the BCI system for 16 times, accuracy of the system was close to its maximum along the experiment. However, regression analysis on typing speed of each sentence demonstrated a significant decrease in all 7 subjects ( ) when thresholds obtained from a calibration test were kept fixed over the experiment. In comparison, no significant change in typing speed was observed when the proposed adaptive algorithms were used. The analysis of variances revealed that the average typing speed of the last four sentences when using adaptive relational algorithm (8.7 char/min) was significantly higher than the tolerance-based algorithm (8.1 char/min) and significantly above 6 char/min when the fixed thresholds were used. Therefore, the relational algorithm proposed in this paper could successfully compensate for the effect of fatigue on performance of the SSVEP BCI system.

An Efficient Asynchronous High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface speller: The Problem of Individual Differences.

BACKGROUND: Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) provide high rates of accuracy and information transfer rate, but need user's attention to flickering visual stimuli. This quickly leads to eye-fatigue when the flickering frequency is in the low-frequency range. High-frequency flickering stimuli (>30 Hz) have been proposed with significantly lower eye-fatigue. However, SSVEP responses in this frequency range are remarkably weaker, leading to doubts about usability of high-frequency stimuli to develop efficient BCI systems. The purpose of this study was to evaluate if a practical SSVEP Speller can be developed with Repetitive Visual Stimuli in the high-frequency range. METHODS: An asynchronous high-frequency (35-40 Hz) speller for typing in Persian language was developed using five flickering visual stimuli. Least absolute shrinkage and selection operator algorithm with two user-calibrated thresholds was used to detect the user's selections. A total of 14 volunteers evaluated the system in an ordinary office environment to type 9 sentences consist of 81 characters with a multistage virtual keyboard. RESULTS: Despite very high performance of 6.9 chars/min overall typing speed, average accuracy of 98.3%, and information transfer rate of 64.9 bpm for eight of the participants, the other six participants had serious difficulty in spelling with the system and could not complete the typing experiment. CONCLUSIONS: The results of this study in accordance with some previous studies suggest that high rate of illiteracy in high-frequency SSVEP-based BCI systems may be a major burden for their practical application.