The existing Devanagari-script-input-based P300 speller (DS-P3S) performs better mostly with 3–15 trials. This leads to poor information transfer rate (ITR) and a major concern in its real-time adaptation. In DS-P3S, the display paradigm is a matrix of $8\times 8$ size which has 28 more characters than the $6\times 6$ English paradigm. The increased number of characters leads to user-related issues such as a crowding effect, double flashing, adjacency distraction, task difficulty, and fatigue which increases the false detection rate. To tackle this, we propose an efficient single-trial character detection approach for DS-P3S using weighted ensemble of deep convolution neural networks (WE-DCNNs). The weighted strategy is constructed based on measured ensemble diversity to counter the instability by the individual classifier. Additionally, to reduce the false detection rate arising from a single trial, a new channel dropout-based character detection approach is introduced first time in this article. The ITR of 55.45 b/min and an average P300 classification accuracy of 92.64% achieved are comparatively higher than existing methods of DS-P3S. The significant reduction in tradeoff between bias and variance for the different subjects affirms the ease of applicability of the proposed model with just a single trial.

中文翻译：

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基于天城体脚本的 P300 拼写器中用于单试字符检测的深度卷积神经网络的加权集成

现有的基于天城文脚本输入的 P300 拼写器 (DS-P3S) 在大多数情况下通过 3-15 次试验表现更好。这导致较差的信息传输率 (ITR) 及其实时适应的主要问题。在 DS-P3S 中，显示范式是一个 $8\times 8$ 大小的矩阵，它比 $6\times 6$ 英文范式多 28 个字符。字符数量的增加会导致与用户相关的问题，例如拥挤效应、双闪、相邻分心、任务难度和疲劳，从而增加误检率。为了解决这个问题，我们使用深度卷积神经网络 (WE-DCNN) 的加权集成为 DS-P3S 提出了一种有效的单次试验字符检测方法。加权策略是基于测量的集合多样性构建的，以对抗个体分类器的不稳定性。此外，为了降低单次试验引起的误检率，本文首次介绍了一种新的基于通道丢失的字符检测方法。55.45 b/min 的 ITR 和 92.64% 的平均 P300 分类精度比现有的 DS-P3S 方法要高。不同受试者的偏差和方差之间的权衡显着减少，这证实了所提出模型的适用性，只需一次试验。