frontier research deep techeeg signal processinghuman subject protocolsneural decodingneurotechnology
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Theoretical command
35% weight
Check command of neural signal fundamentals: EEG/ECoG/spike sorting, P300 and SSVEP paradigms, common spatial patterns, Riemannian decoders, and why non-stationarity degrades within-session accuracy.
Evidence to listen for
Explains the underlying theory at the level the role demands, and can go a layer deeper when pushed
Knows which results are established and which are contested
Distinguishes their own contribution from the field's
Comfortable saying where the theory runs out
Five-point scoring guide
1
Poor
Recites terminology without understanding; cannot go one layer deeper.
2
Needs Improvement
Surface familiarity; conflates established results with speculation.
3
Satisfactory
Solid grasp of the core theory; thin at the frontier.
4
Very Good
Strong command; separates settled results from open questions.
5
Excellent
Explains decoder choices against signal type and electrode count, and names concrete failure modes such as session drift or muscle artefact contamination.
02
Evaluation factor
From theory to hardware or code
30% weight
Probe what they actually built: BCI2000, OpenBCI, Lab Streaming Layer or BCILAB pipelines, electrode montages, closed-loop latency budgets, and bit rates or ITR achieved with real users.
Evidence to listen for
Has built, simulated, or run something real, not only published about it
Knows the gap between the idealised model and the actual apparatus or system
Names the practical constraint that dominates in real conditions
Can describe a result that did not match prediction
Five-point scoring guide
1
Poor
Purely theoretical; no contact with implementation.
2
Needs Improvement
Some exposure but unaware of practical constraints.
3
Satisfactory
Has implemented work; understands the main real-world limits.
4
Very Good
Strong practical record; articulate about theory-versus-reality gaps.
5
Excellent
Describes a working closed-loop system end to end, with measured latency, calibration time, and per-subject accuracy across more than a handful of participants.
03
Evaluation factor
Research judgement
20% weight
Assess how they chose between invasive and non-invasive routes, dropped paradigms that failed pilot testing, and handled IRB approval, consent, and participants with motor impairment.
Evidence to listen for
Chooses problems by tractability and value, not novelty alone
Knows when to abandon a line of work
Reads and evaluates others' results critically
Can say what would falsify their own approach
Five-point scoring guide
1
Poor
Chases novelty; no sense of tractability or when to stop.
2
Needs Improvement
Weak problem selection; persists past the point of value.
3
Satisfactory
Reasonable judgement within a defined programme.
4
Very Good
Selects problems well and knows when to abandon a line.
5
Excellent
Cites a paradigm they abandoned with the pilot data behind it, and treats participant burden and ethics review as design constraints, not paperwork.
04
Evaluation factor
Explaining it to non-specialists
15% weight
Test how they brief clinicians, industrial designers, and regulators: framing decoder confidence, false activation risk, and training burden without lapsing into signal processing jargon.
Evidence to listen for
Explains the work to an engineer, an executive, or a funder without either mystifying or dumbing it down
Writes clearly
Collaborates across disciplines
Makes the case for resources in terms the audience cares about
Five-point scoring guide
1
Poor
Cannot communicate outside their specialism.
2
Needs Improvement
Explanation is either impenetrable or hollow.
3
Satisfactory
Adequate with technical peers; less effective with lay audiences.
4
Very Good
Explains clearly to specialists and non-specialists alike.
5
Excellent
Translates decoder performance into what a user experiences per session, and separates demonstrated capability from speculative neurotech claims.
Put this rubric to work
Score every candidate against the same standard
Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.
