Related Experiment Video
Updated: Jun 11, 2025
Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
Published on: June 26, 2012
Education Research: Competency-Based EEG Education: An Online Routine EEG Examination for Adult and Child Neurology
Fábio A Nascimento1, Hong Gao1, Roohi Katyal1
1From the Division of Epilepsy (F.A.N.), Department of Neurology, Washington University School of Medicine, St. Louis, MO; Department of Neurology (F.A.N., S.V.Y.), Massachusetts General Hospital, Harvard Medical School, Boston; Department of Internal Medicine (H.G.), Wake Forest University School of Medicine, Winston-Salem, NC; Division of Epilepsy (R.K.), Department of Neurology, Louisiana State University Health Shreveport; Department of Neurology (R.M.), Emory University School of Medicine, Atlanta, GA; Department of Neurosurgery (S.R.), University Hospital Erlangen; Department of Neurosurgery (S.R.), University Hospital Halle (Saale), Germany; Department of Neurology (W.O.T.), Mayo Clinic, Jacksonville, FL; Department of Neurology (R.E.S.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Clinical Neurophysiology (S.B.), Danish Epilepsy Center, Dianalund and Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark.
An online electroencephalogram (EEG) examination was developed to assess neurology residents' interpretation skills. The competency-based test demonstrated excellent psychometrics, differentiating experts from residents and showing improved accuracy with more training.
Area of Science:
- Neurology
- Medical Education
- Neurophysiology
Background:
- Published expert consensus-based curricular objectives for routine EEG (rEEG) interpretation in neurology residents.
- Need for a validated, competency-based assessment tool for rEEG interpretation skills.
Purpose of the Study:
- To develop and validate an online, competency-based examination for rEEG interpretation.
- To assess the psychometric properties of the developed rEEG examination.
Main Methods:
- Developed a 30-question multiple-choice online examination based on rEEG curriculum domains.
- Included deidentified EEG images in two montages; assessed accuracy and level of confidence (LOC).
- Disseminated the examination via professional organizations and social media to a diverse group of respondents.
Main Results:
- 922 complete responses from various levels of expertise (experts, fellows, residents, technologists, students).
- Mean scores: 82% (experts), 81% (fellows), 72% (residents).
- Resident accuracy improved with training (67% to 77%), with >8 weeks needed for expert-level performance; LOC increase lagged behind accuracy.
Conclusions:
- The online, competency-based rEEG examination exhibits excellent psychometrics.
- The tool effectively differentiates experienced EEG readers from neurology residents.
- This online examination has the potential to enhance resident EEG education globally.
