估计并逼近无创视觉脑机接口的最大信息速率
Estimating and approaching maximum information rate of noninvasive visual brain-computer interface
AI总结：
本研究通过信息论估计出无创视觉BCI的最大ITR约63bps，提出宽带WN BCI方法，实验达50bps创纪录，为下一代人机交互提供新方向。
摘要：
视觉脑机接口（BCI）的任务是提升信息传输速率（ITR），使其达到高速以应用于现实生活通信。尽管已取得显著进展，但无创视觉BCI的ITR已遭遇瓶颈，目前尚不清楚是否能实现更高的ITR。本研究中，我们探究初级视觉通道的信息速率极限，以探索我们是否能够以及应当如何构建具有更高信息速率的视觉BCI。利用信息论，我们估计在均匀分布白噪声（WN）刺激下，可实现的最大ITR约为63比特每秒（bps）。基于这一发现，我们提出了一种宽带WN BCI方法，与当前基于稳态视觉诱发电位（SSVEPs）的最先进视觉BCI方法相比，该方法拓展了刺激带宽的利用范围。通过实验验证，我们的宽带BCI以7 bps的显著优势优于SSVEP BCI，创下了50 bps的新纪录。这一成果证明了在仅0.1秒的短时间内解码40类无创神经反应的可能性。本研究提出的信息论框架为所有感觉诱发性BCI提供了宝贵见解，为下一代人机交互系统的发展迈出了重要一步。
英文摘要：
The mission of visual brain-computer interfaces (BCIs) is to enhance information transfer rate (ITR) to reach high speed towards real-life communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we investigate the information rate limits of the primary visual channel to explore whether we can and how we should build visual BCI with higher information rate. Using information theory, we estimate a maximum achievable ITR of approximately 63 bits per second (bps) with a uniformly-distributed White Noise (WN) stimulus. Based on this discovery, we propose a broadband WN BCI approach that expands the utilization of stimulus bandwidth, in contrast to the current state-of-the-art visual BCI methods based on steady-state visual evoked potentials (SSVEPs). Through experimental validation, our broadband BCI outperforms the SSVEP BCI by an impressive margin of 7 bps, setting a new record of 50 bps. This achievement demonstrates the possibility of decoding 40 classes of noninvasive neural responses within a short duration of only 0.1 seconds. The information-theoretical framework introduced in this study provides valuable insights applicable to all sensory-evoked BCIs, making a significant step towards the development of next-generation human-machine interaction systems.
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