Thought-to-Text using Generative AI

About the Project

This project explores the development of cutting-edge thought-to-text systems within the realm of brain-computer interface (BCI) technology. The primary objective is to enable the real-time decoding of neural signals into coherent textual outputs by integrating advanced hardware, software, and neuroscience insights. The research focuses on non-invasive neural recording technologies, such as electroencephalography (EEG), to ensure the system is accessible and safe for broad applications. By employing sophisticated signal processing techniques and machine learning models, the project aims to identify and interpret neural patterns associated with thought formation and translate these into meaningful text with high accuracy and speed.

Additionally, this project addresses challenges in noise reduction, individual variability in brain signal patterns, and user adaptability to enhance system robustness. It also investigates personalized calibration methods and adaptive algorithms to improve performance across diverse user populations. The expected outcomes include an innovative framework for thought-to-text conversion, validated through rigorous testing with real-world use cases, particularly for individuals with severe speech or motor impairments.

This work contributes to the broader field of neurotechnology by not only advancing assistive communication tools but also establishing foundational methods for future brain-to-digital interface applications, such as hands-free control systems, cognitive monitoring, and immersive human-computer interaction.

Key Words: thought-to-text, brain-computer interface (BCI), neural decoding, real-time processing, non-invasive neurotechnology, neural signal analysis, assistive communication, machine learning in BCI, electroencephalography (EEG), personalized neural interfaces.

Requirements:

Essential:

1. Bachelor’s or Master’s degree (2:1 or above) in a relevant field such as Computer Science, Neuroscience, Brain Computer Interfaces, or related disciplines.
2. Proficiency in programming languages such as Python and experience with frameworks like TensorFlow or PyTorch.
3. Familiarity with Machine Learning and Deep Learning techniques, particularly as applied to time-series or neural signal analysis.
4. Understanding of research methodologies and experimental design.
5. Strong communication skills.

Desirable:

1. Prior experience in brain-computer interface research, neural signal processing, or related fields.
2. Knowledge of neural recording technologies, such as EEG, and their applications in brain-computer interface research.
3. Familiarity with neuroscience concepts, particularly those related to brain signal decoding.
4. Ability to work independently and collaboratively in an interdisciplinary environment.

How to Apply:

Applications will be processed on a 'first come, first served' basis, and the hiring process will conclude as soon as a suitable candidate is identified. Interested candidates should email Dr Yashar Moshfeghi (yashar.moshfeghi@strath.ac.uk) and include detailing contact information, and motivation, or background and attach an up-to-date CV.