脑机接口中的信息传输速率：迈向紧密集成的共生关系
Information Transfer Rate in BCIs: Towards Tightly Integrated Symbiosis
AI总结：
该研究针对传统脑机接口信息传输速率（ITR）计算的简化模型缺陷，将共生通信介质建模为离散无记忆信道重新定义ITR，通过实验验证信道不对称性对ITR的关键影响，还提出二分类容量求解算法并探讨多分类扩展方案。
摘要：
信息传输速率（ITR），又称有效比特率，是一种常用且应用广泛的信息度量指标，尤其在基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）领域得到普及。该指标将速度与准确率整合为单值参数，可辅助不同BCI研究社区评估和比较各类目标识别算法。计算ITR时，通常默认采用均匀输入分布，以及过于简化的信道模型——这类模型具有无记忆、平稳、对称的特性，且采用离散字母表大小。为准确表征性能并为未来的BCI设计提供端到端设计思路，有必要对ITR开展更深入的研究并重新给出定义。我们将由膝状体视觉通路承载的共生通信介质建模为离散无记忆（DM）信道，并利用修正后的容量表达式重新定义ITR。我们借助有向图的相关结论，刻画了转移统计特性的不对称性与新定义下ITR增益之间的关系，进而得到数据速率性能的潜在边界。我们在两个知名的SSVEP数据集上，对比了两种前沿的目标识别方法。结果表明，与输入分布的变化相比，诱发的DM信道不对称性对实际感知到的ITR影响更大。此外，研究证实新定义下的ITR增益与信道转移统计特性的不对称程度呈负相关。进一步研究显示，对单个输入进行定制可提升感知到的ITR性能。最后，本文提出了一种求解二分类容量的算法，并讨论了如何通过集成技术将该结果扩展至多分类场景。
英文摘要：
The information transmission rate (ITR), or effective bit rate, is a popular and widely used information measurement metric, particularly popularized for SSVEP-based Brain-Computer (BCI) interfaces. By combining speed and accuracy into a single-valued parameter, this metric aids in the evaluation and comparison of various target identification algorithms across different BCI communities. In order to calculate ITR, it is customary to assume a uniform input distribution and an oversimplified channel model that is memoryless, stationary, and symmetrical in nature with discrete alphabet sizes. To accurately depict performance and inspire an end-to-end design for futuristic BCI designs, a more thorough examination and definition of ITR is therefore required. We model the symbiotic communication medium, hosted by the retinogeniculate visual pathway, as a discrete memoryless channel and use the modified capacity expressions to redefine the ITR. We leverage a result for directed graphs to characterize the relationship between the asymmetry of the transition statistics and the ITR gain due to the new definition, leading to potential bounds on data rate performance. On two well-known SSVEP datasets, we compared two cutting-edge target identification methods. Results indicate that the induced DM channel asymmetry has a greater impact on the actual perceived ITR than the change in input distribution. Moreover, it is demonstrated that the ITR gain under the new definition is inversely correlated with the asymmetry in the channel transition statistics. Individual input customizations are further shown to yield perceived ITR performance improvements. Finally, an algorithm is proposed to find the capacity of binary classification and further discussions are given to extend such results to multi-class case through ensemble techniques.
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