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이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해 끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은 과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업. 도구: Datacollect/scripts/link_reconcile_apply.mjs Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
145 lines
5.3 KiB
Markdown
145 lines
5.3 KiB
Markdown
---
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id: wiki-2026-0508-neuroergonomics
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title: Neuroergonomics
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Neuro-Ergonomics, Brain at Work, Cognitive Ergonomics]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [neuroergonomics, hci, cognitive-load, fnirs, eeg]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: mne-python
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---
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# Neuroergonomics
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 measurement modalities
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- **EEG**: 매 ms-level temporal resolution. 매 cognitive load 의 alpha-suppression / theta-Fz 의 marker.
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- **fNIRS**: 매 cortex hemodynamics. 매 portable, motion-tolerant — 매 real-world 의 work.
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- **Eye tracking**: 매 fixation duration, pupil dilation — 매 mental effort 의 proxy.
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- **HRV / GSR**: 매 ANS arousal — 매 stress / engagement.
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### 매 cognitive states 의 detect
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- **Workload**: 매 over-load → 매 error spike. 매 under-load → 매 vigilance drop.
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- **Vigilance / fatigue**: 매 P300 amplitude decline + theta increase.
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- **Engagement / flow**: 매 mid-frontal theta + alpha asymmetry.
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### 매 응용
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1. 매 adaptive cockpit (Airbus, Honeywell): 매 pilot workload 의 high → 매 secondary task 의 defer.
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2. 매 surgical training: 매 trainee fNIRS prefrontal 의 over-activation = novice marker.
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3. 매 driver-state monitoring (Tesla v13, Mercedes Drive Pilot): 매 EEG drowsiness 의 detect.
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## 💻 패턴
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### EEG workload index (theta/alpha ratio)
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```python
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import mne, numpy as np
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raw = mne.io.read_raw_brainvision('subj.vhdr', preload=True)
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raw.filter(1, 40)
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psd = raw.compute_psd(fmin=4, fmax=12, picks=['Fz', 'Pz'])
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freqs = psd.freqs
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power = psd.get_data() # (channels, freqs)
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theta = power[:, (freqs >= 4) & (freqs < 8)].mean(axis=1)
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alpha = power[:, (freqs >= 8) & (freqs < 13)].mean(axis=1)
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workload_index = theta / alpha # higher = more load
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```
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### fNIRS prefrontal activation (MNE-NIRS)
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```python
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from mne_nirs.experimental_design import make_first_level_design_matrix
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from mne_nirs.statistics import run_glm
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raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od, ppf=0.1)
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design = make_first_level_design_matrix(raw_haemo, drift_model='cosine')
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glm = run_glm(raw_haemo, design)
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# beta for HbO in PFC channels = task-evoked activation
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pfc_activation = glm.to_dataframe().query("ch_name.str.contains('S1_D1') & Chroma=='hbo'")
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```
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### Pupil-based effort (PsychoPy + Pupil Labs)
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```python
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import zmq, msgpack
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ctx = zmq.Context(); sub = ctx.socket(zmq.SUB)
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sub.connect('tcp://127.0.0.1:50020'); sub.setsockopt_string(zmq.SUBSCRIBE, 'pupil')
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while True:
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topic, payload = sub.recv_multipart()
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msg = msgpack.unpackb(payload)
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if msg['confidence'] > 0.8:
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diameter_mm = msg['diameter_3d']
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# baseline-corrected pupil dilation = effort proxy
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```
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### Closed-loop adaptive UI (workload-triggered)
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```python
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class AdaptiveDashboard:
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def tick(self, workload_idx: float):
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if workload_idx > 1.5: # high load
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self.hide_secondary_widgets()
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self.enlarge_primary_alert()
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elif workload_idx < 0.6: # under-load → boredom
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self.inject_status_check()
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else:
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self.restore_default()
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```
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### Drowsiness detector (real-time EEG)
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```python
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from scipy.signal import welch
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def is_drowsy(eeg_window, fs=256):
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f, P = welch(eeg_window, fs=fs, nperseg=fs*2)
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theta = P[(f>=4)&(f<8)].mean()
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beta = P[(f>=13)&(f<30)].mean()
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return (theta / beta) > 4.0 # KSS-validated threshold
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```
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### NASA-TLX subjective + neural fusion
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```python
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def fused_workload(neural_idx: float, tlx_score: float) -> float:
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# weight neural higher when within-subject calibrated
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return 0.6 * neural_idx_z + 0.4 * (tlx_score / 100)
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Lab, high precision needed | EEG (32-64ch) + eye-track |
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| Field / mobile work | fNIRS + wearable HRV |
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| Driver / pilot | Webcam-pupil + steering-entropy + PERCLOS |
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| Long shift fatigue | Actigraphy + HRV + PVT |
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**기본값**: fNIRS + eye-tracking — 매 real-world ecological validity 의 best.
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## 🔗 Graph
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- 변형: [[Affective Computing]] · [[Brain-Computer-Interface]]
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## 🤖 LLM 활용
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**언제**: 매 study design review, 매 GLM script generation, 매 multimodal-feature engineering, 매 paper synthesis.
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**언제 X**: 매 raw artifact rejection, 매 individual-subject calibration — 매 expert review 의 require.
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## ❌ 안티패턴
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- **Single-modality reliance**: 매 EEG-only 의 motion artifact 에 fragile. 매 fusion 의 require.
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- **No baseline**: 매 absolute power 의 between-subject 의 noisy. 매 within-subject z-score 의 use.
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- **Open-loop dashboard**: 매 measure-but-not-act → 매 value 의 zero. 매 closed-loop 의 design.
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- **No personalization**: 매 group-mean threshold 의 50% individuals 에 wrong.
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## 🧪 검증 / 중복
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- Verified (Parasuraman & Rizzo 2007 *Neuroergonomics*; Ayaz & Dehais 2019).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — modalities + closed-loop adaptive patterns |
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