# Seed Emotion Recognition Eval

> Evaluates a model's ability to classify EEG signals into three affective states (negative, neutral, positive) using a semi-supervised learning framework. It tests representation learning and classification performance on high-dimensional, noisy time-series data with limited labeled sessions. Use when the user wants to benchmark on SEED, or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/seed-emotion-recognition-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/seed-emotion-recognition-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/seed-emotion-recognition-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/seed-emotion-recognition-eval

---


# seed-emotion-recognition-eval

> Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition — Zhang et al. (2021) (arXiv:2107.13505, 2021)

## What this evaluates

Evaluates a model's ability to classify EEG signals into three affective states (negative, neutral, positive) using a semi-supervised learning framework. It tests representation learning and classification performance on high-dimensional, noisy time-series data with limited labeled sessions.

## Datasets

- **SEED** — total ?; splits: train (9), test (6)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Standard classification accuracy: the proportion of correctly predicted emotion labels (negative, neutral, positive) out of the total number of test instances.

## Input / output format

**Input**: EEG time-series recordings from 62 scalp electrodes sampled at 1 KHz, structured into 15 sessions per experiment (5s pre-stimuli notice + visual stimuli).

**Output**: Predicted emotion label from the set {negative, neutral, positive}.

## Scoring recipe

```python
def compute_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return correct / len(gold_labels)
```

## Common pitfalls

- The dataset is split per subject/experiment (9 sessions train, 6 test), not randomly shuffled across all sessions.
- The evaluation is semi-supervised, meaning the model leverages unlabeled sessions during training alongside a small fraction of labeled data.

## Evidence (verbatim from paper)

> We use the SEED dataset to conduct emotion recognition experiments with three affective labels namely negative, neutral, and positive. ... In each of the 30 experiments, we use the first 9 sessions for training and the remaining 6 sessions for testing, as pre-defined in [[10]].

## Citation

```bibtex
@misc{zhang2021deeprecurrent,
  title={Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition},
  author={Zhang et al. (2021)},
  year={2021},
  note={arXiv:2107.13505}
}
```

- arXiv: 2107.13505