Machine Learning Laboratory, Berlin Institute of Technology, Marchstrasse 23, 10537, Berlin, Germany.
J Neural Eng. 2013 Jun;10(3):036025. doi: 10.1088/1741-2560/10/3/036025. Epub 2013 May 20.
In brain-computer interface (BCI) research, systems based on event-related potentials (ERP) are considered particularly successful and robust. This stems in part from the repeated stimulation which counteracts the low signal-to-noise ratio in electroencephalograms. Repeated stimulation leads to an optimization problem, as more repetitions also cost more time. The optimal number of repetitions thus represents a data-dependent trade-off between the stimulation time and the obtained accuracy. Several methods for dealing with this have been proposed as 'early stopping', 'dynamic stopping' or 'adaptive stimulation'. Despite their high potential for BCI systems at the patient's bedside, those methods are typically ignored in current BCI literature. The goal of the current study is to assess the benefit of these methods.
This study assesses for the first time the existing methods on a common benchmark of both artificially generated data and real BCI data of 83 BCI sessions, allowing for a direct comparison between these methods in the context of text entry.
The results clearly show the beneficial effect on the online performance of a BCI system, if the trade-off between the number of stimulus repetitions and accuracy is optimized. All assessed methods work very well for data of good subjects, and worse for data of low-performing subjects. Most methods, however, are robust in the sense that they do not reduce the performance below the baseline of a simple no stopping strategy.
Since all methods can be realized as a module between the BCI and an application, minimal changes are needed to include these methods into existing BCI software architectures. Furthermore, the hyperparameters of most methods depend to a large extend on only a single variable-the discriminability of the training data. For the convenience of BCI practitioners, the present study proposes linear regression coefficients for directly estimating the hyperparameters from the data based on this discriminability. The data that were used in this publication are made publicly available to benchmark future methods.
在脑机接口（BCI）研究中，基于事件相关电位（ERP）的系统被认为特别成功和稳健。这部分源于重复刺激，它可以抵消脑电图中的低信噪比。重复刺激会导致优化问题，因为更多的重复也会花费更多的时间。因此，最佳重复次数代表了刺激时间和获得的准确性之间的数据相关权衡。已经提出了几种处理这种情况的方法，例如“提前停止”、“动态停止”或“自适应刺激”。尽管它们在患者床边的 BCI 系统中有很高的潜力，但这些方法在当前的 BCI 文献中通常被忽略。本研究的目的是评估这些方法的益处。
本研究首次在人工生成数据和 83 个 BCI 会话的真实 BCI 数据的共同基准上评估了现有的方法，允许在文本输入的上下文中直接比较这些方法。
结果清楚地表明，如果优化刺激重复次数和准确性之间的权衡，BCI 系统的在线性能会得到有益的提高。在评估的所有方法中，对于表现良好的受试者的数据，效果非常好，而对于表现较差的受试者的数据则较差。然而，大多数方法在稳健性方面表现出色，即它们不会将性能降低到简单的不停顿策略的基线以下。
由于所有方法都可以作为 BCI 和应用程序之间的模块实现，因此只需进行最小的更改即可将这些方法纳入现有的 BCI 软件架构中。此外，大多数方法的超参数在很大程度上仅依赖于单个变量——训练数据的可辨别性。为了方便 BCI 从业者，本研究提出了线性回归系数，可以根据可辨别性直接从数据中估计超参数。本研究中使用的数据是公开提供的，以便为未来的方法提供基准。
