Many brain-computer interfaces make use of brain signals that are elicited in response to a visual, auditory or tactile stimulus, so-called event-related potentials (ERPs). In visual ERP speller applications, sets of letters shown on a screen are flashed randomly, and the participant attends to the target letter they want to spell. When this letter flashes, the resulting ERP is different compared to when any other non-target letter flashes. We propose a new unsupervised approach to detect this attended letter. In each trial, for every available letter our approach makes the hypothesis that it is in fact the attended letter, and calculates the ERPs based on each of these hypotheses. We leverage the fact that only the true hypothesis produces the largest difference between the class means. Note that this unsupervised method does not require any changes to the underlying experimental paradigm and therefore can be employed in almost any ERP-based setup. To deal with limited data, we use a block-Toeplitz regularized covariance matrix that models the background activity. We implemented the proposed novel unsupervised mean-difference maximization (UMM) method and evaluated it in offline replays of brain-computer interface visual speller datasets. For a dataset that used 16 flashes per symbol per trial, UMM correctly classifies 3651 out of 3654 letters ($99.92\,\%$) across 25 participants. In another dataset with fewer and shorter trials, 7344 out of 7383 letters ($99.47\,\%$) are classified correctly across 54 participants with two sessions each. Even in more challenging datasets obtained from patients with amyotrophic lateral sclerosis ($77.86\,\%$) or when using auditory ERPs ($82.52\,\%$), the obtained classification rates obtained by UMM are competitive. In addition, UMM provides stable confidence measures which can be used to monitor convergence.

翻译：摘要：许多脑机接口系统利用由视觉、听觉或触觉刺激诱发的脑信号，即事件相关电位（ERP）。在视觉ERP拼写器应用中，屏幕上显示的多组字母会随机闪烁，受试者需关注其想要拼写的目标字母。当目标字母闪烁时，其诱发的ERP与非目标字母闪烁时的响应存在差异。本文提出一种新的无监督方法来检测该目标字母。在每次试验中，针对每个可用字母，该方法假设该字母即为目标字母，并基于每个假设计算ERP。我们利用了以下事实：仅真实假设能使类别均值差异最大化。值得注意的是，该无监督方法无需对底层实验范式进行任何修改，因此几乎适用于所有基于ERP的实验设置。为解决数据有限的问题，我们采用了一个块托普利兹正则化协方差矩阵对背景活动进行建模。我们实现了所提出的无监督均值差异最大化（UMM）新方法，并在脑机接口视觉拼写器数据集的离线回放中进行了评估。对于每个符号每次试验使用16次闪烁的数据集，UMM在25名受试者中正确分类了3654个字母中的3651个（99.92%）。在另一个试验次数更少且试次时长更短的数据集中，UMM在54名受试者（每人两次实验）中正确分类了7383个字母中的7344个（99.47%）。即使在更具挑战性的数据集（如肌萎缩侧索硬化症患者数据，正确率77.86%；或基于听觉ERP的数据，正确率82.52%）中，UMM获得的分类率也具有竞争力。此外，UMM能提供稳定的置信度度量，可用于监测收敛过程。