Precise onset detection of human motor responses using a whitening filter and the log-likelihood-ratio test.

Investigation of the human motor system frequently requires precise determination of the motor response onset indicating the time of movement initiation (e.g., in reaction time experiments). This paper presents a new model-based algorithm for computerized response onset detection in kinematic signals (e.g., joint angle). The response onset is identified as an abrupt change in the (time-varying) parameters of a statistical process model adapted to the measured signal. The accuracy of the algorithm is assessed by statistical simulations, and the performance of the method is compared to the performance of conventional onset detection methods using simulated as well as real kinematic signals. Results show that onset detection can substantially be improved by including a priori knowledge on the physiological background of the measured signals to the decision process.