NONLINEAR FUNCTIONAL-MODELING OF SCALE RECORDED AUDITORY-EVOKED RESPONSES TO MAXIMUM LENGTH SEQUENCES

The purpose of this study was to model the adult human's scalp recorde d evoked response to auditory pulses separated by varying inter pulse intervals (IPIs). The responses modeled probably reflect auditory nerv e and brainstem generators. The subjects were 10 young adult humans wi th normal hearing. They were presented pseudo random sequences of puls es (maximum length sequences, MLSs) in order to characterize their sys tem response. For the stimuli and the responses modeled accounting for temporal nonlinearities (interactions among the pulses) improved mode l performance only marginally. Nonlinear contributions to the models d ecreased with increasing interval between the input pulses. Increasing the memory of the model beyond 20 ms did not increase modeled perform ance dramatically. Model performance varied as a function of minimum I PI (MIPI) of the MLSs. At the shortest MIPI overall model performance deteriorated (due, in part, to a decrease in SNR), but nonlinear effec ts became relatively more important. At the longest MIPI performance a lso deteriorated, possibly due to the increasing influence of longer l atency, more variable evoked potential components. Modeled performance generalized to responses recorded in the same recording session to th e same and different MLSs. This study confirms the similarity between MLS linear kernels and conventionally averaged evoked responses-both a re adapted responses reflecting the IPIs of the evoking stimuli. (C) 1 998 Elsevier Science B.V. All rights reserved.