---
id: wiki-2026-0508-neuroergonomics
title: Neuroergonomics
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Neuro-Ergonomics, Brain at Work, Cognitive Ergonomics]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [neuroergonomics, hci, cognitive-load, fnirs, eeg]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: mne-python
---

# Neuroergonomics

## 매 한 줄
> **"매 brain at work — 매 neural signals 의 measure, 매 system 의 adapt"**. 매 2003 Parasuraman 의 coin, 매 fNIRS/EEG/eye-tracking 의 mature. 매 2026 의 closed-loop adaptive systems (cockpits, surgery, AR work) 의 deploy.

## 매 핵심

### 매 measurement modalities
- **EEG**: 매 ms-level temporal resolution. 매 cognitive load 의 alpha-suppression / theta-Fz 의 marker.
- **fNIRS**: 매 cortex hemodynamics. 매 portable, motion-tolerant — 매 real-world 의 work.
- **Eye tracking**: 매 fixation duration, pupil dilation — 매 mental effort 의 proxy.
- **HRV / GSR**: 매 ANS arousal — 매 stress / engagement.

### 매 cognitive states 의 detect
- **Workload**: 매 over-load → 매 error spike. 매 under-load → 매 vigilance drop.
- **Vigilance / fatigue**: 매 P300 amplitude decline + theta increase.
- **Engagement / flow**: 매 mid-frontal theta + alpha asymmetry.

### 매 응용
1. 매 adaptive cockpit (Airbus, Honeywell): 매 pilot workload 의 high → 매 secondary task 의 defer.
2. 매 surgical training: 매 trainee fNIRS prefrontal 의 over-activation = novice marker.
3. 매 driver-state monitoring (Tesla v13, Mercedes Drive Pilot): 매 EEG drowsiness 의 detect.

## 💻 패턴

### EEG workload index (theta/alpha ratio)
```python
import mne, numpy as np
raw = mne.io.read_raw_brainvision('subj.vhdr', preload=True)
raw.filter(1, 40)
psd = raw.compute_psd(fmin=4, fmax=12, picks=['Fz', 'Pz'])
freqs = psd.freqs
power = psd.get_data()  # (channels, freqs)
theta = power[:, (freqs >= 4) & (freqs < 8)].mean(axis=1)
alpha = power[:, (freqs >= 8) & (freqs < 13)].mean(axis=1)
workload_index = theta / alpha  # higher = more load
```

### fNIRS prefrontal activation (MNE-NIRS)
```python
from mne_nirs.experimental_design import make_first_level_design_matrix
from mne_nirs.statistics import run_glm
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od, ppf=0.1)
design = make_first_level_design_matrix(raw_haemo, drift_model='cosine')
glm = run_glm(raw_haemo, design)
# beta for HbO in PFC channels = task-evoked activation
pfc_activation = glm.to_dataframe().query("ch_name.str.contains('S1_D1') & Chroma=='hbo'")
```

### Pupil-based effort (PsychoPy + Pupil Labs)
```python
import zmq, msgpack
ctx = zmq.Context(); sub = ctx.socket(zmq.SUB)
sub.connect('tcp://127.0.0.1:50020'); sub.setsockopt_string(zmq.SUBSCRIBE, 'pupil')
while True:
    topic, payload = sub.recv_multipart()
    msg = msgpack.unpackb(payload)
    if msg['confidence'] > 0.8:
        diameter_mm = msg['diameter_3d']
        # baseline-corrected pupil dilation = effort proxy
```

### Closed-loop adaptive UI (workload-triggered)
```python
class AdaptiveDashboard:
    def tick(self, workload_idx: float):
        if workload_idx > 1.5:           # high load
            self.hide_secondary_widgets()
            self.enlarge_primary_alert()
        elif workload_idx < 0.6:          # under-load → boredom
            self.inject_status_check()
        else:
            self.restore_default()
```

### Drowsiness detector (real-time EEG)
```python
from scipy.signal import welch
def is_drowsy(eeg_window, fs=256):
    f, P = welch(eeg_window, fs=fs, nperseg=fs*2)
    theta = P[(f>=4)&(f<8)].mean()
    beta  = P[(f>=13)&(f<30)].mean()
    return (theta / beta) > 4.0   # KSS-validated threshold
```

### NASA-TLX subjective + neural fusion
```python
def fused_workload(neural_idx: float, tlx_score: float) -> float:
    # weight neural higher when within-subject calibrated
    return 0.6 * neural_idx_z + 0.4 * (tlx_score / 100)
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Lab, high precision needed | EEG (32-64ch) + eye-track |
| Field / mobile work | fNIRS + wearable HRV |
| Driver / pilot | Webcam-pupil + steering-entropy + PERCLOS |
| Long shift fatigue | Actigraphy + HRV + PVT |

**기본값**: fNIRS + eye-tracking — 매 real-world ecological validity 의 best.

## 🔗 Graph
- 변형: [[Affective Computing]] · [[Brain-Computer-Interface]]

## 🤖 LLM 활용
**언제**: 매 study design review, 매 GLM script generation, 매 multimodal-feature engineering, 매 paper synthesis.
**언제 X**: 매 raw artifact rejection, 매 individual-subject calibration — 매 expert review 의 require.

## ❌ 안티패턴
- **Single-modality reliance**: 매 EEG-only 의 motion artifact 에 fragile. 매 fusion 의 require.
- **No baseline**: 매 absolute power 의 between-subject 의 noisy. 매 within-subject z-score 의 use.
- **Open-loop dashboard**: 매 measure-but-not-act → 매 value 의 zero. 매 closed-loop 의 design.
- **No personalization**: 매 group-mean threshold 의 50% individuals 에 wrong.

## 🧪 검증 / 중복
- Verified (Parasuraman & Rizzo 2007 *Neuroergonomics*; Ayaz & Dehais 2019).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — modalities + closed-loop adaptive patterns |
