Airwriting recognition is a task that involves identifying letters written in free space using finger movement. It is a special case of gesture recognition, where gestures correspond to letters in a specific language. Electroencephalography (EEG) is a noninvasive technique for recording brain activity and has been widely used in brain–computer interface (BCI) applications. Leveraging EEG signals for airwriting recognition offers a promising alternative input method for human–computer interaction. One key advantage of airwriting recognition is that users do not need to learn new gestures. By concatenating recognized letters, a wide range of words can be formed, making it applicable to a broader population. However, there has been limited research in the recognition of airwriting using EEG signals, which forms the core focus of this study. The NeuroAiR dataset comprising EEG signals recorded during writing English uppercase alphabets is first constructed. Various features are then explored in conjunction with different deep learning models to achieve accurate airwriting recognition. These features include processed EEG data, independent component analysis (ICA) components, source-domain-based scout time series, and spherical and head harmonic decomposition (HHD)-based features. Furthermore, the impact of different EEG frequency bands on system performance is comprehensively investigated. The highest accuracy achieved in this study is 44.04% using ICA components and the EEGNet classification model. The results highlight the potential of EEG-based airwriting recognition as a user-friendly modality for alternative input methods in human–computer interaction applications. This research sets a strong baseline for future advancements and demonstrates the viability and utility of EEG-based airwriting recognition.

中文翻译：

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NeuroAiR：根据头皮记录的神经信号进行空中书写识别的深度学习框架

空中书写识别是一项涉及使用手指运动识别自由空间中书写的字母的任务。这是手势识别的特例，其中手势对应于特定语言中的字母。脑电图（EEG）是一种记录大脑活动的无创技术，已广泛应用于脑机接口（BCI）应用中。利用脑电图信号进行空中书写识别为人机交互提供了一种有前途的替代输入方法。空中书写识别的一个关键优势是用户不需要学习新的手势。通过连接识别的字母，可以形成广泛的单词，使其适用于更广泛的人群。然而，利用脑电信号识别空中书写的研究还很有限，这也是本研究的核心焦点。首先构建了由书写英语大写字母期间记录的脑电图信号组成的 NeuroAiR 数据集。然后结合不同的深度学习模型探索各种特征，以实现准确的空中书写识别。这些特征包括处理的 EEG 数据、独立分量分析 (ICA) 分量、基于源域的侦察时间序列以及基于球面和头部谐波分解 (HHD) 的特征。此外，还全面研究了不同脑电图频段对系统性能的影响。本研究中使用 ICA 组件和 EEGNet 分类模型实现的最高准确度为 44.04%。结果凸显了基于脑电图的空中书写识别作为人机交互应用中替代输入方法的用户友好模式的潜力。这项研究为未来的进步奠定了坚实的基础，并证明了基于脑电图的空中书写识别的可行性和实用性。