chore(brain): ASTRA 성장 자산 동기화 — 기능 인벤토리·growth(약점프로필/학습큐)·일화기억·장기기억·회의록 원문

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+---
+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 |