Key result
The HAF-HOC scheme for emotion recognition from EEG signals achieved classification rates up to 85.17% for discriminating six distinct emotions, surpassing previous approaches.
Population
16 healthy subjects
Comparison
Hybrid Adaptive Filtering and Higher Order… vs Previous approaches for emotion recognition from…
Design
Other
Authors
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May enable affective interfaces in clinical systems; extends prior EEG methods but leaves open prospective validation.
The HAF-HOC scheme provides a highly accurate method for emotion recognition from EEG signals, advancing the development of affective human-machine interfaces.
Petrantonakis et al. (2010) studied Emotion recognition (n=16). HAF-HOC (Hybrid Adaptive Filtering and Higher Order Crossings analysis) vs. Previous approaches was evaluated on Emotion recognition classification rate. The HAF-HOC scheme for emotion recognition from EEG signals achieved classification rates up to 85.17% for discriminating six distinct emotions, surpassing previous approaches.
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