Objective: In epileptic disorders, EEG background activity is disorganized in or near the epileptogenic focus and spectral EEG analysis(SEA) can provide usefu l information about the focus. We tried to develop a new spectral index from bas ic spectral parameters to detect the epileptic abnormalities at EEG background a ctivity. Methods: A new spectral EEG index,epileptic abnormality index(EAI), was constructed from frequency band power and power asymmetry parameters.Within the index,parameters were weigh- ted due to both conventional EEG knowledge and their power in discrimination h ealthy subjects from patients. EEG background activity from 99 epileptic patient s and 146 healthy subjects was examined both by EAI and by a conventional SEA me thod, by using z-scoring statistic.Each test results were compared with visual EEG interpretation of subjects. Results: In patient groups,EAI was most successf ul in lateralization of epileptic abnormalities. It was also helpful in discrimi nation of epileptic patients from normals in the case where visual EEG interpret ation was ‘normal’. Conclusions:EAI depends on basic spectral parameters and i t combines statistical methods and clinical knowledge about EEG. It increases th e analysis capacity of SEA in evaluation of EEG background activity. Significanc e: EAI is a new and useful approach in detection of EEG background abnormalities in epilepsy and its logical base can also be used in the detection of brain ele ctrical activity abnormalities other than epileptic disorders. Objective: In epileptic disorders, EEG background activity is disorganized in or near the epileptogenic focus and spectral EEG analysis (SEA) can provide usefu l information about the focus. We tried to develop a new spectral index from bas ic spectral parameters to detect the epileptic Methods: A new spectral EEG index, epileptic abnormality index (EAI), was constructed from frequency band power and power asymmetry parameters. Since the index, parameters were weigh- ted due to both conventional EEG knowledge and their power in discrimination h ealthy subjects from patients. EEG background activity from 99 epileptic patient s and 146 healthy subjects were examined both by EAI and by a conventional SEA me thod, by using z-scoring statistic. Each test results were compared with visual EEG interpretation of subjects. Results: In patient groups, EAI was most successf ul in lateralization of epileptic abnormalities. It was also helpful in discriminami nation of epileptic patients from normals in the case where visual EEG interprettion was ’normal’. Conclusions: EAI depends on basic spectral parameters and it combines statistical methods and clinical knowledge about EEG. It increases th e analysis capacity of SEA in evaluation of EEG background activity. Significanc e: EAI is a new and useful approach in detection of EEG background abnormalities in epilepsy and its logical base can also be used in the detection of brain ele ctrical activity abnormalities other than epileptic disorders.