AlphaHandOpen repo

Results

Quantitative outcomes for AlphaHand EEG finger movement identification are updated regularly as validation runs complete.

Results at a glance

Placeholder metrics below are ready for tomorrow's final value drop-in.

Cross-subject accuracyTODO_ACCURACY_PERCENT%

Macro F1: TODO_F1_MACRO

Subjects: TODO_N_SUBJECTS | Trials: TODO_N_TRIALS

Latency: TODO_LATENCY_MS ms

Methods

Cross-validation protocol: TODO_CV_PROTOCOL

Dataset notes: TODO_DATASET_NOTES

Accuracy trend

AlphaHand EEG accuracy plot showing class-level and aggregate model performance over validation runs
TODO: Caption explaining what the accuracy trend plot shows and why it supports model reliability.

Confusion matrix

AlphaHand confusion matrix for EEG-based finger movement class predictions
TODO: Caption summarizing which finger classes are most separable and where errors are concentrated.
AlphaHand summary chart for EEG finger movement decoding

Methodology summary

Sessions were segmented into overlapping windows, standardized per participant, and evaluated with stratified cross-validation to check generalization across subjects.

Protocol placeholder: TODO_CV_PROTOCOL. Dataset placeholder notes: TODO_DATASET_NOTES.

How to cite

Jonathan Davanzo. TODO_PAPER_TITLE. TODO_VENUE. TODO_PUBLICATION_DATE.

Replace this block with the final formatted citation once submission details are finalized.