BCI Classifier | Neural Implementation

01 — THE PROBLEM

Control through Thought.

Motor imagery BCIs let users control systems just by imagining movement. But there's a catch: some users never achieve reliable control, even after substantial training.

02 — THE BOTTLENECK

The Calibration Trap.

Today's calibration process takes hours. It's costly in time, effort, and frustration. We asked: Can we tell early on who is actually likely to succeed?

03 — THE SOLUTION

A Fast, Low-Sample Filter.

Instead of long sessions, we designed an EEG screening tool using just a brief resting baseline and the first 15 attempts. We focused only on a compact sensorimotor region to keep things fast.

04 — THE MODEL

Distilling the Signal.

Using the PhysioNet dataset (109 subjects), we took 38 complex brain features and distilled them down to the 12 most important predictors. We fed these into a Random Forest machine learning model.

05 — THE RESULTS

It Worked.

78.8% Accuracy at identifying who will struggle — before they waste hours calibrating. A fast, data-efficient first-pass filter.

Pearson r = 0.580 · R² = 0.302 · threshold = 0.65

06 — TRY IT

See It in Action.

Explore the live interactive simulation — watch our model classify real PhysioNet subjects in real-time.

ENTER INTERFACE