EmoWrite: A Sentiment Analysis-Based Thought to Text Conversion -- A Validation Study
Imran Raza, Syed Asad Hussain, Muhammad Hasan Jamal, Isabel de la Torre Diez, Carmen Lili Rodriguez Velasco, Jose Manuel Brenosa, Imran Ashraf
TL;DR
EmoWrite introduces a non-invasive emotion-aware BCI that converts thought to text using a dynamic circular keyboard and an online RNN for context-driven word prediction. By integrating emotion detection (happiness, sadness, anger, calm) with word prediction and auto-completion, the system achieves high accuracy (90.36%), 6.58 WPM, 31.92 CPM, and ITRs of 87.55 bits/min (commands) and 72.52 bits/min (letters) with a 2.685 s latency across 72 volunteers. The approach combines EEG signals from a 14-channel Emotiv Epoc+ headset, motor-imagery-based navigation, and emotion-based word selection to improve speed and usability for communication aids. While promising, the work notes substantial training requirements, potential generalizability limitations, and UI complexity as avenues for future refinement.
Abstract
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. The data required for this experiment was obtained from a total of 72 volunteers (40 male and 32 female) aged between 18 and 40 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.