By Ana Fred, Joaquim Filipe, Hugo Gamboa
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Extra resources for Biomedical Engineering Systems and Technologies (Communications in Computer and Information Science, Volume 52)
Therefore, the baseline matrix was excluded in the final processing pipeline. 3 Feature Selection To achieve good classification results with pattern recognition and machine learning, the set of input features is crucial. This is no different with classifying emotions [7,8,10]. As was denoted in Sect. 2, biosignals can be processed in the time, frequency, timefrequency, and power domain. For EMG and EDA signals, the time domain is most often employed for feature extraction; see also Table 1. Consequently, we have chosen to explore a range of features from the time domain: mean, absolute deviation, standard deviation (SD), variance, skewness, and kurtosis.
3), while being exposed to emotion inducing film fragments; see Sect. 4. See Fig. 1 for an overview of the processing scheme applied in the current research. Subsequently, preprocessing and the automatic classification of biosignals, using the four emotion categories, were presented in Sect. 5 and Sect. 6. Also in this research, the differences among participants became apparent. They can be denoted on four levels; see also Sect. 1. , music or films). Moreover, these four levels interact [7,8,14].
A literature overview is provided of the work done so far, see also Table 1 and Table 2. In addition, some guidelines on affective MMI are provided; see Sects. 1 and 2. To enable the recognition of these emotions, they had to be classified. Therefore, a brief description was provided of the classification techniques used (Sect. 3). Next, a study is introduced in which three EMG signals and people’s EDA were measured (see also Fig. 3), while being exposed to emotion inducing film fragments; see Sect.
Biomedical Engineering Systems and Technologies (Communications in Computer and Information Science, Volume 52) by Ana Fred, Joaquim Filipe, Hugo Gamboa