Brain-Computer Interface Based on High-Frequency Steady-State Visual Evoked Potentials from Below-the-Hairline Areas Thesis - Floriano, Alan Silva da Paz (2019)
Steady-State Visual Evoked Potentials (SSVEPs) are brain responses that present the same frequency (and/or harmonics) of the visual stimulation. Applications, such as BrainComputer Interfaces, can be derived of their properties. SSVEP response is often maximal on the visual cortex area, consequently, most of the existing SSVEP-based BCIs use electrodes located at occipital and parietal regions. However, these areas are generally covered by hair, which cause complications in the electrode contact with the skin. On the other hand, currently, researchers are looking at how to transfer BCIs from the lab to the patient’s home. Recent studies have reported the use of below-the-hairline areas, such as behind-the-ears (temporal area), with stimuli in low/medium frequency bands, to control BCI systems, which suggests that measuring the EEG from hairless areas presents key advantages for technology transfer. However, the visual stimuli in low/medium frequencies used in these studies can produce visual fatigue and other problems to users. This thesis presents studies about characterization of SSVEP response from below-the-hairline areas in high-frequency, with the aim of developing a practical BCI without generating discomfort to users. First, results of our research indicate that SSVEP response from hairless areas are influenced by the reference electrode position, and that the best configuration to measure this response is temporal-frontal montage (TP9-Fpz and TP10-Fpz). The second important result found in our research was that chromatic and luminance stimuli elicit strong SSVEP on the hairless areas, and that the SSVEP response is related to frequency and stimuli color. Results indicate that green-red stimulus elicits the highest SSVEP response in the medium-frequency range (15-25 Hz). On the other hand, green-blue stimulus elicits the highest SSVEP at high-frequencies (30-40 Hz). In addiction, results show that a combination of colors and luminance enhance the SSVEP detection accuracy. Another important contribution of our research was the combination of high-frequency SSVEP (from below-the-hairline areas) with eye focusing mechanism (Depth-of-Field) to command a robot in a virtual environment. In online tests, the volunteers achieved an success rate of 96%. These findings contribute to state of the art, and the development of more practical and comfortable BCIs.