CURVEX is a brain scanner that captures the raw EEG signal from the prefrontal (FP1 electrode) position and based on a FFT (fast fourier transform), our application acquires data from the individual frequency bands (gamma, beta, alpha, theta, delta). The prefrontal cortex monitors the regulation of emotion, impulsivity and attention/focus and is primarily involved with motivation, mood regulation, planning and organization of thoughts and actions.
We aim to develop a neurofeedback (NFB) mobile application that tracks levels of attention from the prefrontal cortex and translates the acquired signal into an real-time output to improve levels of focus, decrease impulsivity and reduce symptoms of ADHD (attention deficit hyperactivity disorder). Research suggests that persons with ADHD reflect low levels of arousal in frontal brain areas, with excess of theta waves and deficit of beta waves. We will first accurately track the level of attention in each user using the frequency bands, following which will use machine learning (ML) to focus these specific frequencies (and research other techniques) to build an adaptive NFB training that regulates the level of attention and levels of arousal of the user through real-time feedback. and thereby reduce ADHD symptoms. Regulating these mental states has shown to indicate improvements in attention, which indirectly result in the reduction of hyperactivity and impulsive behaviour. In addition to tracking their brain activity (EEG), we have thoughts of adding pupillometrics captured using the phone’s camera to aid in more accurate tracking of levels of attention, distraction and impulsivity in the user while interacting with the NFB training.
The ML model to track levels of attention will be initially tested on an online dataset, before implementing the same into the CURVEX headset. Our app-developers will provide the software expertise to build the elements in the app, while you will focus on the data processing and analysis to build the core functionality of the NFB application. Attention-based NFB training can significantly reduce impulsivity, anxiety and emotional outbursts, which are all closely linked to ADHD, epilepsy, schizophrenia and bipolar disorder. Through this application, we seek to address a pressing health need surrounding mental health and levels of attention.
- A bachelors or master’s degree or higher university degree in data science, cognitive science, machine learning, AI, computer science, statistics, applied mathematics or related technical fields
- Solid programming skills in Python and knowledge of algorithms and data structures
- Exceptional English skills, both spoken and in writing.
- Passion for ML/AI and experience with one or several of the deep learning frameworks like TensorFlow, PyTorch or Scikit-Learn
- Experience with model validation, accuracy, verification and visualization
- Have knowledge of math, probability, statistics, and algorithms
- Apply data mining techniques using state-of-the-art research, methods, pinpointing trends, correlations, and patterns in a complicated data set
- Manage data pipelining surrounding the development of ML model
- Independent worker and curious scientific/research oriented mindset
- Past experience working with bio signals (EEG, ECG, HR etc.) or signal processing experience would be a plus
Don't hesitate to reach out to Sachin Prathaban (firstname.lastname@example.org or at + 45 53 33 39 36) if you have any questions regarding this project or if want to learn more about CURVEx.
We look forward to hearing from you and welcoming you as part of our team on this mind bending journey :)