The objective of the present study was to identify the origin(s) of ageing‐related behavioural slowing in sensorimotor tasks. For this aim, event‐related potentials (ERPs) were analysed at 64 electrodes to evaluate the strength and timing of different stages of information processing in the brain. Electrophysiological indices of stimulus processing, sensorimotor integration/response selection and motor‐related processing were used to compare the processing speed of young (n = 13, mean age = 22.5 years) and older adults (n = 14, mean age = 58.3 years) in simple‐ and choice‐reaction tasks presented in two modalities, auditory and visual. The behavioural results showed significant ageing‐related slowing, but only in the choice‐reaction task. The quantification of separate central processing stages, in combination with advanced ERP methodology, helped to reveal that this slowing did not originate from the early processes of stimulus processing and response selection. Instead, it was produced by slower activation patterns over the contralateral motor cortex underlying response generation. It is concluded that ageing is accompanied by a functional dysregulation of motor cortex excitability during sensorimotor processing, with this deficit becoming progressively evident with greater task complexity.

A time–frequency decomposition was applied to rare target and frequent non-target event-related potentials (ERPs) elicited in an oddball condition to assess whether multiple functional components occur in the P300 latency range. The wavelet transform (WT) was used because it allows capture of simultaneous or partly overlapping components in ERPs without loosing their temporal relationships. The application of a four-octave quadratic B-spline wavelet transform at the level of single-sweep data allowed us to obtain new information and revealed the presence of separate events during P300 development. Several delta, theta, and alpha frequency components in the P300 latency range differed between target and non-target processing. These findings indicate that P300 is composed of multiple functional components and that the WT method is of use for the study of P300 functional correlates more precisely.