Lenhardt Alexander, Kaper Matthias, Ritter Helge J
University of Bielefeld, Bielefeld, Germany.
IEEE Trans Neural Syst Rehabil Eng. 2008 Apr;16(2):121-30. doi: 10.1109/TNSRE.2007.912816.
The P300 component of an event related potential is widely used in conjunction with brain-computer interfaces (BCIs) to translate the subjects intent by mere thoughts into commands to control artificial devices. A well known application is the spelling of words while selection of the letters is carried out by focusing attention to the target letter. In this paper, we present a P300-based online BCI which reaches very competitive performance in terms of information transfer rates. In addition, we propose an online method that optimizes information transfer rates and/or accuracies. This is achieved by an algorithm which dynamically limits the number of subtrial presentations, according to the subject's current online performance in real-time. We present results of two studies based on 19 different healthy subjects in total who participated in our experiments (seven subjects in the first and 12 subjects in the second one). In the first, study peak information transfer rates up to 92 bits/min with an accuracy of 100% were achieved by one subject with a mean of 32 bits/min at about 80% accuracy. The second experiment employed a dynamic classifier which enables the user to optimize bitrates and/or accuracies by limiting the number of subtrial presentations according to the current online performance of the subject. At the fastest setting, mean information transfer rates could be improved to 50.61 bits/min (i.e., 13.13 symbols/min). The most accurate results with 87.5% accuracy showed a transfer rate of 29.35 bits/min.
事件相关电位的P300成分广泛应用于脑机接口（BCI），用于将受试者仅通过思维产生的意图转化为控制人工设备的指令。一个著名的应用是单词拼写，通过将注意力集中在目标字母上来选择字母。在本文中，我们展示了一种基于P300的在线BCI，其在信息传输速率方面具有非常有竞争力的性能。此外，我们提出了一种在线方法来优化信息传输速率和/或准确率。这是通过一种算法实现的，该算法根据受试者当前的在线实时表现动态限制子试验呈现的次数。我们展示了两项基于总共19名不同健康受试者的研究结果，这些受试者参与了我们的实验（第一项研究中有7名受试者，第二项研究中有12名受试者）。在第一项研究中，一名受试者实现了高达92比特/分钟的峰值信息传输速率，准确率为100%，平均为32比特/分钟，准确率约为80%。第二项实验采用了动态分类器，该分类器能让用户根据受试者当前的在线表现通过限制子试验呈现的次数来优化比特率和/或准确率。在最快设置下，平均信息传输速率可提高到50.61比特/分钟（即13.13符号/分钟）。准确率最高为87.5%时，传输速率为29.35比特/分钟。
