Objective. The traditional uniform flickering stimulation pattern shows strong steady-state visual evoked potential (SSVEP) responses and poor user experience with intense flicker perception. To achieve a balance between performance and comfort in SSVEP-based brain-computer interface (BCI) systems, this study proposed a new grid stimulation pattern with reduced stimulation area and low spatial contrast. Approach. A spatial contrast scanning experiment was conducted first to clarify the relationship between the SSVEP characteristics and the signs and values of spatial contrast. Four stimulation patterns were involved in the experiment: the ON and OFF grid stimulation patterns that separately activated the positive or negative contrast information processing pathways, the ON-OFF grid stimulation pattern that simultaneously activated both pathways, and the uniform flickering stimulation pattern that served as a control group. The contrast-intensity and contrast-user experience curves were obtained for each stimulation pattern. Accordingly, the optimized stimulation schemes with low spatial contrast (the ON-50% grid stimulus, the OFF-50% grid stimulus, and the Flicker-30% stimulus) were applied in a 12-target and a 40-target BCI speller and compared with the traditional uniform flickering stimulus (the Flicker-500% stimulus) in the evaluation of BCI performance and subjective experience. Main results. The OFF-50% grid stimulus showed comparable online performance (12-target, 2 s: 69.87±0.74 vs. 69.76±0.58 bits min-1, 40-target, 4 s: 57.02±2.53 vs. 60.79±1.08 bits min-1) and improved user experience (better comfortable level, weaker flicker perception and higher preference level) compared to the traditional Flicker-500% stimulus in both multi-targets BCI spellers. Significance. Selective activation of the negative contrast information processing pathway using the new OFF-50% grid stimulus evoked robust SSVEP responses. On this basis, high-performance and user-friendly SSVEP-based BCIs have been developed and implemented, which has important theoretical significance and application value in promoting the development of the visual BCI technology.
Objectives. Metal-wire electrode arrays are widely used to record and stimulate neurons. Commonly, these devices are fabricated from a long insulated metal wire by cutting it into the proper length and using the cross-section as the electrode site. The assembly of a micro-wire electrode array with regular spacing is difficult. With the help of micro-machine technology, a silicon-based wire electrode array (SWEA) is proposed to simplify the assembling process and provide a wire-type electrode with tapered tips. Approach. Silicon wires with regular spacing coated with metal are generated from a silicon wafer through micro-fabrication and are ordered into a 3D array. A silicon wafer is cut into a comb-like structure with hexagonal teeth on both sides by anisotropic etching. To establish an array of silicon-based linear needles through isotropic wet etching, the diameters of these hexagonal teeth are reduced; their sharp edges are smoothed out and their tips are sharpened. The needle array is coated with a layer of parylene after metallization. The tips of the needles are then exposed to form an array of linear neural electrodes. With these linear electrode arrays, an array of area electrodes can be fabricated. Main results. A 6 × 6 array of wire-type electrodes based on silicon is developed using this method. The time required to manually assemble the 3D array decreases significantly with the introduction of micro-fabricated 2D array. Meanwhile, the tip intervals in the 2D array are accurate and are controlled at no more than 1%. The SWEA is effective both in vitro and in vivo. Significance. Using this method, the SWEA can be batch-prepared in advance along with its parameters, such as spacing, length, and diameter. Thus, neural scientists can assemble proper electrode arrays in a short time.
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Background: In the past 20 years, neural engineering has made unprecedented progress in the interpretation of brain information (e.g., brain-computer interfaces) and neuromodulation (e.g., electromagnetic stimulation and neurofeedback). However, the study of improving the performance of the brain-computer interface (BCI) using the neuromodulation method rarely exists. The present study designs a neurofeedback training method to improve the performance of steady-state visual evoked potential (SSVEP) BCI and further explores its underlying mechanisms. Methods: As parietal lobe is the sole hub of information transmission, up-regulating alpha-band power of the parietal lobe by neurofeedback training was present as a new neural modulating method to improve SSVEP-based BCI in this study. Results: After this neurofeedback training (NFT), the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of SSVEP-based BCI were increased by 5.8%, 4.7%, and 15.6% respectively. However, no improvement has been observed in the control group in which the subjects do not participate in NFT. What’s more, a general reinforcement of the information flow from the parietal lobe to the occipital lobe was also observed. Evidence from the network analysis and attention test further indicate that NFT improves attention via developing the control ability of the parietal lobe and then enhances the above SSVEP indicators. Conclusion: Up-regulating parietal alpha-amplitude using neurofeedback training significantly improves the SSVEP-baesd BCI performance through modulating the control network. The study validates an effective neuromodulation method, and possibly also contributes to explaining the function of the parietal lobe in the control network.
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