Simple Neurofeedback via Machine Learning: Challenges in real time multivariate assessment of meditation state
bioRxiv Preprint Server September 27, 2022 Sruthi Susan Kuriakose, Aishwarya Swamy, Rahul Venugopal et al. 2 citations preprint
Meditation proficiency is hard to achieve without feedback because the mind easily wanders. EEG neurofeedback could help by providing real-time assessment. This work proposes a lightweight scheme using an autoencoder model trained on EEG features from long-term meditators. The model runs in real time on short data segments from a few channels, using reconstruction errors or latent variables as feedback parameters to measure meditation ability. However, results show that meditation states overlap substantially in multivariate EEG features and have prominent temporal dynamics, which simple one-class algorithms fail to capture. Multiple improvements to the autoencoder are described to address these issues and enable high-precision neurofeedback protocols.