Electroencephalography (EEG)-based brain computer interface (BCI) enables people to interact directly with computing devices through their brain signals. A BCI typically interprets EEG signals to reflect the user’s intent or other mental activity. Motor imagery (MI) is a commonly used technique in BCIs where a user is asked to imagine moving certain part of the body such as a hand or a foot. By correctly interpreting the signal, one can perform a multitude of tasks such as controlling wheel chair, playing computer games, or even typing text. However, the use of motor-imagery-based BCIs outside the laboratory environment is limited due to the lack of their reliability. This work focuses on another kind of mental imagery, namely, the visual imagery (VI). VI is the manipulation of visual information that comes from memory. This work presents a deep convolutional neural network (DCNN)–based system for the recognition of visual/mental imagination of English alphabets so as to enable typing directly via brain signals. The DCNN learns to extract the spatial features hidden in the EEG signal. As opposed to many deep neural networks that use raw EEG signals for classification, this work transforms the raw signals into band powers using Morlet wavelet transformation. The proposed approach is evaluated on two publicly available benchmark MI-EEG datasets and a visual imagery dataset specifically collected for this work. The obtained results demonstrate that the proposed model performs better than the existing state-of-the-art methods for MI-EEG classification and yields an average accuracy of 99.45% on the two public MI-EEG datasets. The model also achieves an average recognition rate of 95.2% for the 26 English-language alphabets.

Graphical abstract

Overall working of the proposed solution for imagined character recognition through EEG signals

中文翻译：

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使用深度卷积神经网络通过 EEG 信号进行想象的字符识别

基于脑电图 (EEG) 的脑机接口 (BCI) 使人们能够通过大脑信号直接与计算设备交互。BCI 通常解释 EEG 信号以反映用户的意图或其他心理活动。运动意象 (MI) 是 BCI 中常用的技术，其中要求用户想象移动身体的某些部分，例如手或脚。通过正确解释信号，人们可以执行多种任务，例如控制轮椅、玩电脑游戏，甚至输入文本。然而，由于缺乏可靠性，在实验室环境之外使用基于运动意象的 BCI 是有限的。这项工作侧重于另一种心理意象，即视觉意象（VI）。VI是对来自记忆的视觉信息的操纵。这项工作提出了一种基于深度卷积神经网络 (DCNN) 的系统，用于识别英语字母的视觉/心理想象，从而能够通过大脑信号直接打字。DCNN 学习提取隐藏在 EEG 信号中的空间特征。与许多使用原始 EEG 信号进行分类的深度神经网络不同，这项工作使用 Morlet 小波变换将原始信号转换为波段功率。所提出的方法在两个公开可用的基准 MI-EEG 数据集和专门为此工作收集的视觉图像数据集上进行了评估。获得的结果表明，所提出的模型比现有的最先进的 MI-EEG 分类方法表现更好，并且在两个公共 MI-EEG 数据集上的平均准确率为 99.45%。

图形概要

通过 EEG 信号进行想象字符识别的拟议解决方案的整体工作