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Both classes of emotions first require us to infer what a person's life as a whole must be like.
All samples were assessed by a large group of human evaluators (experts and volunteers) and labeled into the abovementioned classes of emotions, summarized in Table 1.
The creation of a new multi-label dataset with 72 music features for 593 songs categorized into one or more out of 6 classes of emotions.
It was evaluated using 63 channel EEG recordings (28 pairs, seven center electrodes) from five healthy subjects with a 256 Hz sampling rate and a band-pass filter between 0.05 and 70 Hz with five different classes of emotions (disgust, happy, surprise, sad, and fear).
Results also suggested some blends between activation patterns associated with different classes of emotions, particularly for feelings of Wonder or Transcendence.
This analysis revealed activations patterns for each of the 3 super-ordinate classes of emotions that were very similar to those described above (see Supplementary Fig. S1).
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Tenfold cross-validation testing was performed for six and seven classes of emotion on the CK+ dataset.
A recognition accuracy of 97.2% was achieved for seven classes of emotion using a person independent testing scheme.
A recognition rate of 91.1% was achieved for seven classes of emotion using a sevenfold cross-validation testing scheme.
In [19], a 10-fold cross-validation testing procedure was used on six and seven classes of emotion in the CK dataset.
Tables 1 and 2 summarize the recognition rate of GLTP and our proposed method, Improved GLTP, for six and seven classes of emotion in the CK+ dataset.
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CEO of Professional Science Editing for Scientists @ prosciediting.com