Emotion Recognition Based on Fusion of Local Cortical Activations and Dynamic Functional Networks Connectivity: An EEG Study

In this paper, we present a method to improve emotion recognition based on the fusion of local cortical activations and dynamic functional network patterns. We estimate the cortical activations using power spectral density (PSD) with the Burg autoregressive model. On the other hand, we estimate the...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.143550-143562
Main Authors: Al-Shargie, Fares, Tariq, Usman, Alex, Meera, Mir, Hasan, Al-Nashash, Hasan
Format: Article
Language:English
Subjects:
Autoregressive models
Band-pass filters
Brain modeling
classification
cortical activation
electroencephalogram (EEG)
Electroencephalography
Emotion
Emotion recognition
Emotions
Frequencies
functional connectivity network patterns
fusion
Locking
Networks
Physiology
Power spectral density
Signal resolution
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Summary:In this paper, we present a method to improve emotion recognition based on the fusion of local cortical activations and dynamic functional network patterns. We estimate the cortical activations using power spectral density (PSD) with the Burg autoregressive model. On the other hand, we estimate the functional connectivity networks by utilizing the phase locking value (PLV). The results of cortical activations and connectivity networks show different patterns across three emotions at all frequency bands. Similarly, the results of fusion significantly improve the classification rate in terms of accuracy, sensitivity, specificity and the area under the receiver operator characteristics curve (AROC), p < 0.05. The average improvement with fusion in all evaluation metrics are 6.84% and 4.1% when compared to PSD and PLV alone, respectively. The results clearly demonstrate the advantage of fusion of cortical activations with dynamic functional networks for developing human-computer interaction system in real-world applications.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2944008