QA Group Report

QA Group Report#

QA Group reports aggregate quality assessment metrics across all subjects in a single dataset. They help identify cohort-level patterns, outliers, and task-dependent quality shifts.

For execution instructions, see the Tutorial.

What QA Group Reports Show#

The QA Group report answers the question: “What are the quality patterns across my entire dataset?”

While QA Subject reports focus on individual recordings, QA Group reports reveal:

Dataset-wide metric distributions
Subject and recording outliers
Task/condition-dependent quality differences
Spatial patterns across the sensor array

Navigation Structure#

The QA Group report uses a two-level tab hierarchy:

Top tabs: Combined (mag+grad) | MAG | GRAD
  └── Section tabs:
      ├── Summary distributions
      ├── Cohort QA overview
      ├── QA metrics across tasks
      ├── QA metrics details
      └── Cumulative distributions

Top-Level Channel Type Tabs#

Tab	Content
`Combined (mag+grad)`	Pooled view across all channel types (use with caution due to unit mixing)
`MAG`	Magnetometer-specific analysis (units: pT)
`GRAD`	Gradiometer-specific analysis (units: pT/m)

Section 1: Summary Distributions#

This section provides quick statistical overviews of each metric across all recordings.

What you’ll see:

Violin plots: Show full distribution shape for each metric
Box plots: Highlight median, quartiles, and outliers
Individual points: Each recording plotted for identification

How to interpret:

Wide distributions suggest high variability across recordings
Outlier points above/below whiskers may need individual inspection
Compare MAG vs GRAD tabs for sensor-type-specific patterns

Section 2: Cohort QA Overview#

This section provides integrated cohort summaries for quick triage.

Components:

Recording-by-Metric Heatmap#

Rows = recordings, columns = metrics
Color = normalized burden (dark = higher burden)
Hover for raw values

Subject Ranking Table#

Subjects ranked by aggregated quality footprint
Higher rank = more quality issues
Click to identify problematic subjects

Top Subject Epoch Profiles#

Small multiples showing epoch-wise patterns for highest-burden subjects
Quickly identify temporal patterns in problematic recordings

Section 3: QA Metrics Across Tasks#

This section reveals how quality varies by task or experimental condition.

What you’ll see:

Separate distributions per task/condition
Subject trajectories connecting the same subject across conditions
Statistical comparison of condition effects

How to interpret:

Parallel trajectories suggest consistent within-subject patterns
Crossing trajectories suggest condition-specific effects
Large between-condition shifts may indicate task-related artifacts

Section 4: QA Metrics Details#

This section provides deep-dive visualizations for each metric.

Available views per metric:#

Metric	Views
STD	Distributions, fingerprint scatters, channel×epoch heatmaps, topomaps
PtP	Distributions, fingerprint scatters, channel×epoch heatmaps, topomaps
PSD	Frequency burden distributions, mains ratio distributions
ECG/EOG	Correlation burden distributions, topomaps
Muscle	Event burden distributions

Channel×Epoch Heatmaps#

These heatmaps aggregate channel×epoch patterns across subjects:

Rows = channels, columns = epochs
Color = metric value
Top profile = epoch summary, right profile = channel summary

Pooled Topomaps#

Sensor-space visualizations showing where quality issues concentrate:

2D flat topomaps for quick viewing
3D interactive topomaps for detailed exploration

PSD Frequency Views#

Show spectral patterns across the cohort:

Mains frequency burden
Harmonic patterns
Broadband contamination

Section 5: Cumulative Distributions#

Statistical appendix with empirical cumulative distribution functions (ECDFs).

How to use:

Compare distribution tails across metrics
Identify what percentage of recordings exceed specific thresholds
Support threshold selection for QC decisions

Practical Reading Order#

Start in Summary Distributions → Get quick overview of metric spreads
Move to Cohort QA Overview → Identify outlier subjects/recordings
Check QA Metrics Across Tasks → Test for task-dependent patterns
Use QA Metrics Details → Explain observed outliers with detailed views
Use Cumulative Distributions → Support threshold decisions

Tips for Effective Use#

Always compare MAG and GRAD tabs when investigating issues
Use hover information to identify specific subjects/recordings
Cross-reference with QA Subject reports for detailed inspection of flagged recordings
Document findings before proceeding to QC Group analysis