Electroencephalography (EEG) and evoked potentials (EPs) are used in psychiatry and neurology as useful, non-invasive and inexpensive tests of the brain's neuronal function. Although good progress has been made in extending the sensitivity and specificity of EEGs and EPs using quantitative computerized methods, the lack of rigorous automated methods for detecting artifacts is claimed to be a major obstacle inhibiting correct routine clinical application of these developments. The Phase I feasibility study was intended to determine whether automated artifact detection could be substantially improved. A data base of normal and abnormal EEGs from 12 patients and healthy subjects was assembled and scored for artifact. Analyzing this data base with detectors implemented during Phase I, 99 percent of eye movement, 99 percent of muscle potential and 98 percent of movement artifacts were found, with false detections of 2 percent, 4 percent and 7 percent, respectively. The applicant concluded that performance could be improved by designing detectors which further distinguish artifact types and subtypes. The development of the full artifact processing system is proposed for Phase II.
