发表机构
Lake Washington High School; Stony Brook University（华盛顿湖高中; 石溪大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于BrainFlow/LSL的硬件无关实时P300采集流水线，跨五款EEG设备验证，发现Flex信号最佳，解码器AUC约0.72，字符留出准确率31.3%。
AI 中文摘要
P300拼写器为严重运动障碍患者（如肌萎缩侧索硬化症）提供了有效的沟通渠道，并且仍然是最成熟的无需手术的皮层内接口替代方案之一。先进的语言模型使拼写器更快、更稳健，但其底层硬件却研究不足。我们提出了一种基于BrainFlow和Lab Streaming Layer（LSL）的硬件无关、实时P300采集流水线，该流水线可在消费级和研究级EEG头戴设备上无需修改地运行，并采用置换检验评估信号可分性。使用标准的6×6行列范式，我们试点了五种配置：定制干电极系统、定制湿/凝胶系统、Emotiv Flex、Emotiv EPOC X和Muse 2。定制系统和EPOC X表现出较弱或不一致的可分性，Muse 2尽管中央-顶叶覆盖有限但采集可靠性最高，而Flex显示出最有前景的信号。在另外20次Flex会话中，改变受试者、时间和短语长度（131个目标字符），峰值幅度置换检验和交叉验证的xDAWN解码器均在两种通道排除策略下检测到显著的目标响应，解码器在15次重复后AUC达到约0.72。字符准确率高度依赖评估方法：样本内多数投票达到94.7%，而字符留出准确率为31.3%，且跨重复累积证据，约为留出多数投票的三倍。这些分析表明，在研究条件下Flex捕获了可检测的（尽管仍较弱）P300信号，而更广泛的参与者级验证和改进解码仍然必要。
英文摘要
P300 spellers offer people with severe motor impairment, such as ALS, an effective communication channel and remain one of the most established surgery-free alternatives to intracortical interfaces. Advanced language models have made spellers faster and more robust, yet the hardware beneath them is under-studied. We present a hardware-agnostic, real-time P300 acquisition pipeline built on BrainFlow and Lab Streaming Layer (LSL) that runs unchanged across consumer- and research-grade EEG headsets, with permutation tests of signal separability. Using a standard 6 x 6 row/column paradigm, we piloted five configurations: a custom dry system, a custom wet/gel system, Emotiv Flex, Emotiv EPOC X, and Muse 2. The custom systems and EPOC X showed weak or inconsistent signal separability, Muse 2 had the highest acquisition reliability despite limited centro-parietal coverage, and Flex showed the most promising signal. In 20 further Flex sessions varying subject, timing, and phrase length (131 target characters), a peak-amplitude permutation test and a cross-validated xDAWN decoder both detected a significant target response under two channel-exclusion policies, with decoder AUC reaching about 0.72 after 15 repetitions. Character accuracy depended heavily on evaluation methodology: in-sample majority voting reached 94.7%, whereas character-held-out accuracy was 31.3% with evidence accumulated across repetitions, about three times that of held-out majority voting. These analyses indicate that Flex captured a detectable, if still weak, P300 under the studied conditions, while broader participant-level validation and improved decoding remain necessary.
