The detection and treatment of artifacts in computer-controlled neurophysiological experiments

Pattern recognition algorithms may be employed on time-series data obtained in electrophysiological experiments to detect particular artifacts that would otherwise contaminate the experimental data. Such algorithms may be classified by the statistical properties of the data they test. Four representative algorithms are presented that, on the basis of prior experiments in our laboratory, perform satisfactorily to improve the quality of electrophysiological measurement.