Individual skill
brain-computer-interfaces
BCI design and evaluation — signal decoding, calibration, closed-loop paradigms, and performance metrics.
skills/brain-computer-interfaces/SKILL.md
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## Overview
brain-computer-interfaces covers the engineering and neuroscience of systems that translate neural
activity into control signals: invasive ( Utah arrays, Neuropixels, ECoG) and non-invasive (EEG,
fNIRS) BCIs for communication, motor restoration, and control. It focuses on the closed loop —
decoding must work in real time, adapt to non-stationary brains, and be evaluated on metrics that
reflect actual usability, not offline accuracy.
A BCI is not a decoder; it is a system comprising signal acquisition, preprocessing, decoding,
feedback, and a user learning to use it. Failures usually live in the loop, not the classifier.
## When to use
- Choosing a BCI paradigm: motor imagery, P300 spellers, SSVEP, ECoG high-gamma, intracortical