Quality estimation of subdurally recorded, event-related potentials based on signal-to-noise ratio

• Published in: IEEE transactions on biomedical engineering 2002-01, Vol.49 (1), p.31-40
• Main Authors: Rohde, M.M., BeMent, S.L., Huggins, J.E., Levine, S.P., Kushwaha, R.K., Schuh, L.A.
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Biological and medical sciences; Biomedical engineering; Biomedical measurements; Brain - physiopathology; Brain computer interfaces; Communication system control; Electrodiagnosis. Electric activity recording; Electroencephalography; Enterprise resource planning; Epilepsy - physiopathology; Evoked Potentials; Female; Human subjects; Humans; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Middle Aged; Motor Cortex - physiopathology; Nervous system; Noise measurement; ROC Curve; Signal Processing, Computer-Assisted; Signal to noise ratio; User-Computer Interface
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/10.972837

Our goal is to develop a direct brain interface (DBI) that will provide communication and environmental control to persons who are "locked-in" (or nearly so) as a consequence of brainstem stroke, amyotrophic lateral sclerosis (ALS), or other etiologies. Previously we demonstrated that templates constructed from trigger averaged event-related potentials (ERPs) can be cross-correlated with ongoing electrocorticograms (ECoGs) to detect ERPs associated with the performance of simple motor actions. However, it was difficult to predict a priori which of many candidate ECoG recording site(s) could provide signals that would provide adequate motor action detection. We present here a measure of ERP quality based on an estimate of the signal to noise ratio (SNR) associated with the formation of an ERP template from the performance of consecutive voluntary actions. Detection-theory-based receiver operator characteristics (ROCs) and a database of ECoGs (6000+) recorded from the cortical surface of awake human subjects were used to assess the usefulness of the SNR technique. The SNR method was found to predict the detection efficacy of ERPs when characterized over a wide parameter range, with the majority of ROC curve areas greater than 90%. This method was compared with our previously developed quality measure (the peak-to-baseline ratio) and found to provide significantly better performance (ROC area differences from 4.4% to 13.7%). Thus, the SNR estimate of the ERP is a useful tool to predict the efficacy of ERP templates for cross-correlation-based detection and assist in the selection of viable ERP templates for DBI applications.