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Brain-Computer Interface (BCI) User Experience Researcher interview scorecard

Evaluate Brain-Computer Interface (BCI) User Experience Researcher candidates across 4 weighted areas: theoretical command, from theory to hardware or code, research judgement, and explaining it to non-specialists. Theoretical command leads at 35%, so check command of EEG/ECoG signal basics, ITR and bits-per-minute metrics, P300 and SSVEP paradigms, plus NASA-TLX, SUS and workload. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For theoretical command, look for evidence the candidate explains signal artefacts, calibration drift and decoder retraining alongside validated workload scales, without conflating usability metrics with decoding accuracy. For from theory to hardware or code, look for evidence the candidate names specific devices and study builds they ran, including participant counts, session length and how findings changed the decoder or interface. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
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 EEG/ECoG signal basics, ITR and bits-per-minute metrics, P300 and SSVEP paradigms, plus NASA-TLX, SUS and workload instruments used in neuroergonomics studies.

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 signal artefacts, calibration drift and decoder retraining alongside validated workload scales, without conflating usability metrics with decoding accuracy.

02
Evaluation factor

From theory to hardware or code

30% weight

Probe hands-on work with actual headsets or implant studies: OpenBCI, g.tec, Neurable, LSL streaming, Psychopy or Unity task builds, and IRB-approved protocols they wrote.

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

Names specific devices and study builds they ran, including participant counts, session length and how findings changed the decoder or interface.

03
Evaluation factor

Research judgement

20% weight

Assess how they choose between within-subject crossover designs and longitudinal single-case work when N is five, and how they handle participants with ALS or tetraplegia.

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

Justifies small-N designs, defines stopping criteria, and treats non-transfer between able-bodied pilots and clinical users as a real risk.

04
Evaluation factor

Explaining it to non-specialists

15% weight

Test how they brief hardware engineers and clinicians: journey maps, calibration-burden findings, and translating a drop in classifier confidence into a design change.

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

Turns neural and behavioural data into a concrete design recommendation clinicians and firmware engineers both act on, with caveats stated plainly.

Evidence-led prompts

Interview questions for a Brain-Computer Interface (BCI) User Experience Researcher

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Can you provide examples of past projects related to neurological or cognitive devices?

  2. 02

    What experience do you have with human-computer interaction research?

  3. 03

    Can you detail your experience with usability testing in the context of these interfaces?

  4. 04

    How familiar are you with quantitative and qualitative research methods?

  5. 05

    How do you approach designing experiments to test these user interfaces?

See the complete Brain-Computer Interface (BCI) User Experience Researcher question set
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