Comment by D-Machine

> The proposed SoC was extensively validated on two intracranial electroencephalography (iEEG) datasets related to psychiatric disorders, achieving 80.9%/74.8% sensitivity and 74.2%/73.1% specificity on the multi-source interference task (MSIT) and the emotional conflict resolution (ECR) task, respectively. We further evaluated the SoC on the public DEAP dataset for electroencephalography (EEG)-based emotion recognition, achieving 70.5%/72.5% sensitivity and 69.8%/66.3% specificity for the arousal and valence states, respectively. [https://ieeexplore.ieee.org/document/11685197]

I dunno, really need to see the comparative performance of other classifiers and the base rates in these datasets to know whether these performance metrics are anything we should care about, and whether e.g. a classifier on simple verbal probes to the subjects might simply yield better predictions than going through the EEG signals. You can do some really interesting EEG biofeedback stuff, so maybe this is useful, but I would think the full EEG head-gear would also provide much richer and more useful information than a measurement based on a single chip. Presumably this would be for psychiatric disorders or brain injuries where the core dysfunction we know is limited to a very specific region(s).