Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America.
J Neural Eng. 2017 Aug;14(4):046025. doi: 10.1088/1741-2552/aa7525.
The role of a brain-computer interface (BCI) is to discern a user's intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable.
We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP.
With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional row-column paradigm.
By accounting for refractory effects, an information-theoretic approach can be exploited to significantly improve BCI performance across a wide range of performance levels.
脑-机接口（BCI）的作用是通过从脑信号中提取和解码相关信息来识别用户的意图信息或动作。刺激驱动的 BCI，如 P300 拼写器，依赖于检测与用户关注相关或目标刺激事件相关的事件相关电位（ERP）。然而，这个过程容易出错，因为 ERP 嵌入在嘈杂的脑电图（EEG）数据中，这代表了在嘈杂传输过程中接收到的信息的不确定性的通信中的一个基本问题。BCI 可以建模为一个噪声通信系统，可以利用信息论方法设计刺激呈现范式，以最大限度地提高呈现给用户的信息量。然而，以前专注于设计纠错码的方法由于低估了心理生理因素对 P300 ERP 诱发过程的影响，以及其提出的方法对在线性能预测能力有限，未能提供显著的性能改进。最大信息率有利于选择具有增加目标呈现频率的刺激呈现模式，这加剧了不应期效应，并在异类范式的背景下对性能产生负面影响。需要一种信息论方法来理解信息量和可靠性之间的基本权衡。
我们通过调整刺激呈现范式的特定参数来开发基于性能的范式（PBP），以在最小化不应期效应的同时最大限度地提高性能。我们使用基于概率的性能预测方法作为评估标准来选择 PBP 的最终配置。
通过我们的 PBP，与传统的行-列范式相比，我们在线性能的准确性和拼写速度都有了显著的提高。
通过考虑不应期效应，可以利用信息论方法在广泛的性能水平上显著提高 BCI 的性能。
