Thesis - Instituto de Telecomunicações

4.2. ELECTRODERMAL ACTIVITY 79The difference of these two values, f t ′ 1− f t ′ 3, is used to compute a in equation 4.44,using 4.47 and 4.48 and solving it in order to a.Determination of SCL. The SCL is computed by subtracting the detected events fromthe EDA signal and producing a low-pass filtered signal to clean some minor misalignment.This algorithm works for series of overlapped SCR events, detecting small variationsin the signal. Observing figures 4.9 to 4.12, it can be seen that the second derivativezeros (or the first derivative sequential maximum and minimum) guide the identification ofSCR events in overlapping cases or in relatively low amplitude events masked by a higheramplitude events.Algorithm 1: Processing steps.123456Filter the signal with low pass filter (5Hz);Detect second order derivative zeros (t 1 and t 3 ) (eq. 4.42)for each pair of zeros: doCompute a, b and t 0 (eq: 4.44 to 4.45)endCompute SCLModel ValidationTo validate the proposed model we used data collected in a psycophysiological study thatinvolved the set of cognitive activities, described in chapter 3. In this work we do nothave a model for the stimulus, and we assume that electrodermal activity exists duringthe realization of the cognitive tests. Monitoring the electrodermal activity during therealization of the cognitive tests, SCRs were automatically extracted by our algorithm.Results and DiscussionThe proposed algorithm was tested against the data collected while the users were performinga cognitive test. In total, 3484 SCR events were detected.Our tests and analysis of results are targeted at assumptions validation made on themorphology of the signal, in particular the SCR shape and the temporal properties related