Classification of Selective Attention based on Steady-State Somatosensory Evoked Potentials using High-Frequency Vibration Stimuli

In recent years, the steady-state somatosensory evoked potential (SSSEP)-based brain-computer interfaces (BCIs) have been developed for assisting people with physical disabilities. However, due to its no well-designed standard tactile vibration stimulator to elicit the SSSEP, the SSSEP-based BCI has not been widely used to real-world applications. In this study, we aim to SSSEP-based BCI with high-frequency vibration stimuli. In our experiments, the stimuli were constructed using the coin vibration motor which controlled by the Arduino. The feasibility of the high-frequency vibration stimuli to elicit SSSEP was validated with SSSEP data from two healthy participants. For data collection, the vibration stimuli were attached on the index finger of the left and right hand. And the participant concentrated on one of the vibration stimuli following the visual commands. For the data analysis, the spatial features were extracted from the acquired EEG signals using the common spatial pattern (CSP) filtering. The regularized linear discriminant analysis (RLDA) also used as a classifier. The experimental results showed that the user's selective attention to one of the high-frequency vibration stimuli was classified with averaged 61.7±9.4%. Based on the experimental results, we can conclude that the SSSEP can elicit by the high-frequency vibration stimuli and it can be used to the BCI.