MillennialScientist

This is the part of the equation I did my PhD on a few years ago. The simple explanation is that we use a combination statistical time series analysis and machine learning on the electrical activity over time to find patterns that correspond to certain intentions or mental states for that individual. However, like you said, the brain is adapting while you learn to control the interface, so those same patterns are always changing. It remains a big topic in this field how we improve machine learning algorithms to adapt to the adapting brain while guiding the adaptation if the brain to create something of a closed system. You'll see this referred to as co-adaptive brain-computer interfaces or Open-Ended Brain-computer interfaces.

Sadly, no. In 5-10 years, you could use a neutral interface to replace a few controller inputs, but it would probably have a 10-20% error rate. You might be able to do things like detect when someone attention gets diverted by a sound and direct the VR to that stimulus, but there are probably easier ways to do that too. Right now the field is a little stuck figuring out what can be done with this technology that cant simply be done better with a simpler technology, for someone who is not completely paralyzed.

Just for clarity, these aren't advantages that are unique to random forests at all. Instead, with a dataset like yours, any choice of standard classical classifier should have performed similarly. The random forest is nice because it lends itself to interpretability of feature importance through the GINI coefficient, and doesnt require a separate feature selector.

I'm wondering why you called it AI in the post though? In the machine learning community, we wouldnt call this AI. I'm not sure if you're aware, but the public perception that this kind of thing is AI has been harmful to our field.

This is just fNIRS, which is a common tool in the field, but really not that great. It's the kind of clumsy technology that OP is working to get us past.

I somewhat agree, and I cant wait to use new invasive hardware, but the key word here is "will". We dont know when, we dont know if our software methods will carry over well, and we don't know what the capabilities of a given modality will be.