Objective- The objective of this study is to introduce EmoWrite, a novel brain-computer interface (BCI) system aimed at addressing the limitations of existing BCI-based systems. Specifically, the objective includes improving typing speed, accuracy, user convenience, emotional state capturing, and sentiment analysis within the context of BCI technology. Method- The method involves the development and implementation of EmoWrite, utilizing a user-centric Recurrent Neural Network (RNN) for thought-to-text conversion. The system incorporates visual feedback and introduces a dynamic keyboard with a contextually adaptive character appearance. Comprehensive evaluation and comparison against existing approaches are conducted, considering various metrics such as accuracy, typing speed, sentiment analysis, emotional state capturing, and user interface latency. Results- EmoWrite achieves notable results, including a typing speed of 6.6 Words Per Minute (WPM) and 31.9 Characters Per Minute (CPM) with a high accuracy rate of 90.36%. It excels in capturing emotional states, with an Information Transfer Rate (ITR) of 87.55 bits/min for commands and 72.52 bits/min for letters, surpassing other systems. Additionally, it offers an intuitive user interface with low latency of 2.685 seconds. Conclusion- The introduction of EmoWrite represents a significant stride towards enhancing BCI usability and emotional integration. The findings suggest that EmoWrite holds promising potential for revolutionizing communication aids for individuals with motor disabilities.

翻译：目的：本研究旨在介绍EmoWrite，一种新型脑机接口系统，旨在解决现有基于BCI系统的局限性。具体目标包括在BCI技术背景下提升打字速度、准确率、用户便利性、情感状态捕捉能力及情感分析性能。方法：该方法涉及EmoWrite系统的开发与实现，采用以用户为中心的循环神经网络进行思想到文本的转换。系统整合了视觉反馈机制，并引入具有上下文自适应字符呈现的动态键盘。通过综合考虑准确率、打字速度、情感分析、情感状态捕捉及用户界面延迟等多维度指标，对系统进行了全面评估并与现有方法进行了对比。结果：EmoWrite取得了显著成果，其打字速度达到每分钟6.6词和每分钟31.9字符，准确率高达90.36%。在情感状态捕捉方面表现优异，指令和字母的信息传输率分别达到87.55比特/分钟和72.52比特/分钟，优于其他系统。此外，系统提供了延迟仅为2.685秒的直观低延迟用户界面。结论：EmoWrite的提出标志着在提升BCI可用性与情感融合方面迈出了重要一步。研究结果表明，EmoWrite在革新运动功能障碍患者的沟通辅助工具方面具有广阔前景。