Algorithm Decodes Mood State from EEG

For those of us who have been engaged in the field of EEG biofeedback and neurotherapies in general, this announcement has a double edged impact. I am both thrilled and irritated at such an announcement as it offers us some important ways to think and develop strategies for enhancing treatment models and irritated that there wasn't a single reference to QEEG or neurofeedback in the paper or references. Obviously, much more work is needed to engage the neuroscience and biomedical engineering community and the evidence is there to construct more sensitive measurement and feedback systems that can then provide us with machine learning technology that will facilitate continuous iprocess mprovement. QMF is investing in BMI systems that can discriminate and quantize human intention to control digital outputs so the algorithms being established here can be leveraged in developing future generations of neural prosthetics.

https://viterbischool.usc.edu/news/2018/09/researchers-decode-mood-from-human-brain-signals/