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Fig. 6 | Brain Informatics

Fig. 6

From: Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata

Fig. 6

Arousal and valence accuracies and required vector storage for the various memory optimization as compared to unoptimized for a AMIGOS and b DEAP data sets. Optimizations include ‘unoptimized’ with distinct iM and FP vectors for all channels, ‘iM vectors constant per modality’ with the same iM vectors between different modalities, ‘FP constant per feature channel’ with the same FP vectors between channels of the same modality, and ‘Rule 90 generation’ with generated FP and iM vectors on top of the previous optimizations

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