EEG Finger Movement Identification Research

Decoding finger intent from a wearable brainwave headband for robotic hand control.

Results snapshot

2-m16 · 2026-03-19

Results

Featured run

89.79% action

85.96% finger on non-rest

Primary holdout

84.66% joint accuracy

98.37% Rest TPR · 18.57% applicability FP

Pseudo-live replay

95.37% would-send precision

86.42% committed joint · 0.25% false rest actuation

Published corpus

4,953 test windows

4,532 non-rest across 2 published runs

AlphaHand robot hand prototype

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Featured figures

Current figures and visuals

The featured bundle includes confusion, calibration, replay, and latent-space artifacts. Use the results page for labels, caveats, and source links.

Open results
Action confusion matrix for 2-m16
Action confusion matrix
Finger confusion matrix for 2-m16
Finger confusion matrix on non-rest windows

PCA · Full dataset

Full-dataset PCA colored by true finger

Each point is one EEG window, projected into three principal components and colored by the labeled finger.

Separated regions suggest structured learned organization, while overlap marks similar or harder windows.

Latest update

2-M16 electrode ablation study

Read the dated release note, then use the results page for the current verified metrics.