Predicting individual responses to neurofeedback is incredibly important. Outcomes can vary widely between people, making it challenging to know who will benefit from training. EEG and cognitive performance data are quantitative and well-suited for statistical and machine-learning models, which can help identify likely responders even with modest sample sizes. Continuous research in this area is essential to improve the effectiveness of neurofeedback. This project specifically focuses on early EEG changes and aims to understand how they can predict long-term cognitive improvements through personalized neurofeedback.
Can't see the pdf? Click here