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.

本论文提出了一种新的无监督方法，利用事件相关电位信号实现脑机接口，开发并应用成功的分类算法 UMM，在不改变实验方法的前提下基于视觉贡献最大的信号进行字母分类，获得优秀的分类结果。

UMM：无监督平均差异最大化

UMM: Unsupervised Mean-difference Maximization

We advance an information-theoretic model of human language processing in the
brain, in which incoming linguistic input is processed at two levels, in terms
of a heuristic interpretation and in terms of error correction. We propose that
these two kinds of information processing have distinct electroencephalographic
signatures, corresponding to the well-documented N400 and P600 components of
language-related event-related potentials (ERPs). Formally, we show that the
information content (surprisal) of a word in context can be decomposed into two
quantities: (A) heuristic surprise, which signals processing difficulty of word
given its inferred context, and corresponds with the N400 signal; and (B)
discrepancy signal, which reflects divergence between the true context and the
inferred context, and corresponds to the P600 signal. Both of these quantities
can be estimated using modern NLP techniques. We validate our theory by
successfully simulating ERP patterns elicited by a variety of linguistic
manipulations in previously-reported experimental data from Ryskin et al.
(2021). Our theory is in principle compatible with traditional cognitive
theories assuming a `good-enough' heuristic interpretation stage, but with
precise information-theoretic formulation.

该文提出了一个基于信息理论的人类语言处理模型，观点是内在的语言输入在启发式解释和错误校正的两个层次上处理，由此提出两种信息处理的脑电信号（N400 和 P600）和由现代自然语言处理技术获得的 surprisal 量。该理论通过成功模拟实验证据，可以为传统的认知理论提供精确的信息理论解释。