Department of Electronic Information Engineering, Nanchang University, Nanchang, People's Republic of China.
State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, People's Republic of China.
J Neural Eng. 2023 Aug 31;20(4). doi: 10.1088/1741-2552/acf242.
. Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) often struggle to balance user experience and system performance. To address this challenge, this study employed stimuli in the 55-62.8 Hz frequency range to implement a 40-target BCI speller that offered both high-performance and user-friendliness.. This study proposed a method that presents stable multi-target stimuli on a monitor with a 360 Hz refresh rate. Real-time generation of stimulus matrix and stimulus rendering was used to ensure stable presentation while reducing the computational load. The 40 targets were encoded using the joint frequency and phase modulation method, offline and online BCI experiments were conducted on 16 subjects using the task discriminant component analysis algorithm for feature extraction and classification.. The online BCI system achieved an average accuracy of 88.87% ± 3.05% and an information transfer rate of 51.83 ± 2.77 bits minunder the low flickering perception condition.. These findings suggest the feasibility and significant practical value of the proposed high-frequency SSVEP BCI system in advancing the visual BCI technology.
基于稳态视觉诱发电位(SSVEP)的脑-机接口(BCI)通常在用户体验和系统性能之间难以平衡。为了解决这一挑战,本研究采用 55-62.8Hz 频率范围内的刺激来实现一个 40 目标的 BCI 拼写器,同时具有高性能和用户友好性。
本研究提出了一种在 360Hz 刷新率的显示器上呈现稳定多目标刺激的方法。实时生成刺激矩阵和刺激渲染用于确保稳定呈现,同时降低计算负载。40 个目标采用联合频率和相位调制方法进行编码,离线和在线 BCI 实验在 16 名受试者上进行,使用任务判别成分分析算法进行特征提取和分类。在线 BCI 系统在低闪烁感知条件下实现了平均准确率为 88.87%±3.05%和信息传输率为 51.83±2.77bit/min。这些发现表明,所提出的高频 SSVEP BCI 系统在推进视觉 BCI 技术方面具有可行性和重要的实际价值。