7 Automatic classification of affective signals

concerning the data set used here, I kindly refer to Chapters 3 and 4.

The remaining chapter is organized as follows: First, in Section 7.3, I will briefly introduce the preprocessing techniques employed. Next, in Section 7.4, the specifications of the pattern recognition techniques will be discussed as well as the classification results they delivered. In Section 7.5, I reflect on my work and critically review it. Finally, in Section 7.6, I will end by drawing the main conclusions.

7.2 Data set

The research in which the data was gathered is already thoroughly documented in Chapters 3 and 4. Therefore, we will only provide a brief summary of it.

The data set concerns the data of 24 subjects who watched movie scenes while affective signals were recorded. In parallel, 4 affective signals were recorded: the EDA and three facial EMG. See Figure 7.2 for samples of the three EMG signals and the EDA signal. These are known to reflect emotions [360]; see also both Table 1.1 and Table 2.4. Regrettably, the affective signal recordings of 3 subjects either failed or were distorted. Hence, the signals of 21 subjects remained for classification purposes.

To elicit emotions with the participants, I selected 8 movie scenes (120 sec. each) for their emotional content. For specifications of these movie scenes, see Chapters 3 and 4. The 8 movie scenes were categorized as being neutral or triggering positive, negative, or mixed (i.e., simultaneous negative and positive; [92]) emotions; hence, 2 movie scenes per emotion category. This categorization was founded on Russell’s valence-arousal model [372, 566, 647].

A TMS International Porti 5 − 16/ASD system was used for the biosignal recordings and was connected to a PC with TMS Portilab software. Three facial EMGs were recorded: the right corrugator supercilii, the left zygomaticus major, and the left frontalis muscle. The EMG signals were high-pass filtered at 20 Hz, rectified by taking the absolute difference of the two electrodes, and average filtered with a time constant of 0.2 sec. The EDA was recorded using two active skin conductivity electrodes and average filtering with a time constant of about 2 sec. See Figure 7.2 for samples of the three EMG signals and the EDA signal.

7.2.1 Procedure

After the participant was seated, the electrodes were attached and the recording equipment was checked. The 8 movie scenes were presented to the participant in pseudo-random order.

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