Let’s connect

THE AI INTELLIGENCE LAYER

The signal is data.
The context is the work.

Large language models can connect a study’s recording summaries, diaries, protocols and review history. The goal is not a more confident chatbot. It is a more inspectable research brief.

THE LANGUAGE-MODEL LAYER

More than a summary.
A review with a memory.

Explore how the AI layer connects recording summaries, diaries, protocols and prior review decisions—without hiding uncertainty.

CHOOSE A RESEARCH QUESTION

Ask across sources.
Go back to the evidence.

LONGITUDINAL CONTEXT

PREPARED EXAMPLE

More reviewable minutes. A different method. Not yet a comparable trend.

N-03 has 48 more reviewable minutes than N-02, but its analysis version also changes the exclusion criteria. The AI needs to reconcile the method note, coverage register and reviewer context before calling the difference a study finding.

  1. Checked calculation: 440 − 392 = 48 more reviewable recording minutes.
  2. Method conflict: N-01 / N-02 use version 1.2; N-03 uses version 1.3.
  3. Next check: assess consistent processing and comparability. Do not infer better sleep from higher recording coverage.
DRAFT OUTPUTCross-night review brief

THE AI RESEARCH WORKSPACE

Six ways to make
the context useful.

Six connected capabilities bring recording summaries, study context and review history together. Each has defined inputs, reviewable outputs and a human decision boundary.

Longitudinal reasoningRead the whole study, not just last night.

Bring multiple recording summaries, quality flags, participant notes, and prior reviewer decisions into one contextual review. Separate a recurring observation from a gap in the data.

CONTEXT IN
Versioned session summaries, coverage records, diaries and annotations
WORK OUT
Cross-night narrative with explicit comparability limits
REVIEW POINT
Confirm that analysis methods and recording conditions support the comparison.
Diary & annotation alignmentConnect the signal story to the human account.

Extract reported events from a sleep diary or interview. Align approximate time references with recording intervals while preserving uncertainty and disagreement between sources.

CONTEXT IN
Consented diaries, study interviews and event annotations
WORK OUT
An aligned event register and questions needing clarification
REVIEW POINT
Do not infer symptoms, emotions or causation from a trace or a sentence alone.
Source-grounded study questionsAn answer with somewhere to go back to.

Retrieve relevant passages from an approved study context. Draft an answer that points to the recording summary, reviewer note, or protocol section behind each material statement.

CONTEXT IN
Authorized study records, source intervals and approved protocols
WORK OUT
A cited response separating observations, interpretations and unknowns
REVIEW POINT
Check citation support, source authority and unresolved evidence.
Protocol & documentation reviewFind what the review packet is missing.

Compare a study packet with its approved documentation requirements. Surface missing identifiers, absent approvals, mismatched versions, and unrecorded decisions for the study coordinator.

CONTEXT IN
Study-specific checklists, session metadata and review status
WORK OUT
Documentation exception list and a coordinator handoff
REVIEW POINT
A software flag is not an ethics approval or regulatory determination.
Research knowledge synthesisKeep the literature separate from the participant.

Organize a team-approved library of papers and method notes. Compare research questions, study populations, measurement methods and limitations without presenting a publication as evidence about an individual.

CONTEXT IN
Approved papers, method documents and structured research questions
WORK OUT
A referenced literature matrix and limitations summary
REVIEW POINT
Verify source access, quotation accuracy, applicability and publication context.
Structured reports & handoffsFrom a long review to a useful next step.

Draft research summaries, reviewer questions and study-coordination briefs using a defined schema. Carry source links, limitations, version references and ownership into the handoff.

CONTEXT IN
Reviewed findings, source references and the intended audience
WORK OUT
An editable brief, question list or structured JSON draft
REVIEW POINT
An authorized person approves the final interpretation and distribution.

DIFFERENT MODELS. DIFFERENT RESPONSIBILITIES.

Signal processing is not
language reasoning.

A responsible system should distinguish what was measured, what an algorithm estimated, and what a language model wrote.

1
Acquisition

The device records.

In-ear EEG sensing research, session identity, timing, contact conditions and acquisition metadata.

The physical measurement layer
2
Signal analysis

Algorithms characterize.

Quality screening, exclusions and versioned signal-derived features, evaluated against the intended research task.

Task-specific models and explicit calculations
3
Contextual AI

Language models connect.

Authorized summaries, diaries, protocols and review history assembled into cited questions, comparisons and drafts.

Long-context reasoning and retrieval
4
Qualified review

People interpret.

Inspect the evidence, resolve uncertainty, approve interpretation and decide what is appropriate to share.

Human authority and accountability

No standalone language model is presented as a raw EEG decoder, clinical decision-maker or substitute for device validation.

EVALUATE WHAT THE MODEL ACTUALLY DOES

Fluent is not
the acceptance criterion.

Before a connected deployment, define the sources, intended users, output schema and failure conditions. Evaluate the review—not just the wording.

CHECKQUESTION TO ASKWHAT MUST STAY VISIBLE
Grounding

Does each material statement have relevant source support?

References, intervals and source type
Contradictions

Does the output preserve disagreement between records?

The conflict, not a guessed resolution
Missing information

Does the model say what it cannot establish?

Exclusions, unknowns and missing notes
Numerical integrity

Are calculations explicit and reproducible?

Units, definitions and calculation basis
Human approval

Can a draft be mistaken for a completed decision?

Reviewer, version and approval status

THE NEXT QUESTION IS WORTH EXPLORING

Let’s bring more
context to the signal.

Start a research conversation