Imran Raza

Imran Raza, Syed Asad Hussain, Muhammad Hasan Jamal et al.

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.

Sana Yasin, Syed Asad Hussain, Sinem Aslan et al.

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.