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feat: OpenEEGBench submission flow (arena runs the eval)
Replace the leftover LLM-leaderboard submission with a first-class OpenEEGBench flow. Users submit a model reference (model_cls + checkpoint + finetuning strategy/head/datasets); the backend validates it, enforces a 5-per-month per-user cap, and queues a request to braindecode/requests. A worker (stub) runs oeb.benchmark() and maps results to braindecode/contents. Backend: - submission_config: OEB enums/defaults (single source of truth) - submissions: pure validation, request building, monthly cap - results_mapping: OEB DataFrame -> contents schema (balanced_accuracy) - model_validation: EEG/OEB weights-source + license checks - OEB-shaped submit endpoint/service; eval worker stub (run_eval_worker.py) Frontend: - OEB submission form (model_cls, weights source, strategies, heads, PEFT modules, datasets, seeds, contact/visibility) with conditional fields - rewritten submission guide (5/month, help -> OpenEEGBench issues, citation) - queue/limit copy aligned; fixed duplicate style key…4db3be0
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Fill model metadata in sample_results, drop unused fields
Fill #Params (M) with backbone-only param counts (head/final_layer excluded), instantiated from open-eeg-bench backbone configs: BIOT=3.19M, LaBraM=5.82M, CBraMod=4.92M, REVE=69.19M, EEGPT=null (no config in backbones.py). Set Hub License to bsd-3-clause for all entries. Drop embed_dim (dataset-dependent), trainable_params (dataset-dependent), and Weight type (Open LLM Leaderboard artefact, not relevant for EEG PEFT submissions) from the data and from every place that consumed them (transform_data, columnUtils, defaults.js, submission form, ModelService eval-queue path).8d3e1e1
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Add braindecode logo side-by-side and remove adapter emojis
Display the braindecode logo next to the eegdash brain logo in the header, linking to braindecode.org. Replace all adapter method emoji icons with plain text labels across the leaderboard, about page, submission form, and filters.862c36c -
Align frontend and backend with adapter_finetuning paper
Replace all LLM-specific references with real EEG benchmark data: - 8 EEG datasets (BCIC-2a, PhysioNet, ISRUC, TUAB, TUEV, CHB-MIT, FACED, SEED-V) - 7 adapter methods (LoRA, IA3, AdaLoRA, DoRA, OFT, Probe, Full Fine-tune) - 7 foundation models (LaBraM, EEGPT, BIOT, BENDR, SignalJEPA, CBraMod, REVE) - Remove official providers, MoE, flagged, merged features - Update params from billions to millions scale - Add real citations and dataset descriptions from the paper5763bf2 -
Migrate from Gradio to React + FastAPI architecture
Replace the old Gradio-based leaderboard with a React frontend and FastAPI backend, adapted from the Open LLM Leaderboard codebase for EEG model evaluation. Backend: FastAPI app with leaderboard, model submission, and voting endpoints. Frontend: React + MUI with leaderboard table, filters, model submission, voting, about page, and citations. Infrastructure: Dockerfile, docker-compose, and env configuration for HF Spaces.ee472e7