Precision in neural movement decoding is mostly about doing a bunch of boring engineering correctly, then accepting that brains are messy wet weather systems anyway.

Here’s what actually moves the needle.

1) Get a signal that can support precision

You can’t “ML” your way out of physics.

• Implants (Utah arrays, ECoG): high spatial/temporal resolution, best fine-grained kinematics.
• MEG: great signals, but the machine is the size of a small guilt complex.
• EEG: doable, cheap-ish, but precision is limited for fine movement. It shines more with coarse intents (left/right, start/stop) unless you use clever constraints.

If your measurement blurs sources together, different intentions will look “similar” no matter how smart your decoder is.

2) Define “movement” in a way that’s decodable

“Move your arm naturally” is not a label, it’s a poem.

You get precision by choosing targets like:

• continuous kinematics (vx, vy, vz; joint angles; cursor velocity),
• discrete states (reach target A/B/C),