# 3.6. Other Architectures

Besides the more common architectures, some researches have explored different specialized models for emotion recognition, each with unique strengths and limitations.

**Deep Echo State Networks (ESNs)** \[124][^1], \[125][^2] use fixed, randomly connected hidden layers to process temporal data efficiently but lack the adaptability of models like LSTMs, making them less suitable for complex EEG data.

**Deep Residual Networks (ResNets)** \[126][^3] feature skip connections to overcome the vanishing gradient problem, proving highly effective in image tasks but generally resource-intensive for EEG signal processing.

**Spiking Neural Networks (SNNs)** \[127][^4], \[128][^5] mimic biological neuron firing, promising for bio-inspired applications, but their complex training requirements limit practical application in EEG-based emotion recognition.

Lastly, **Deep Belief Networks (DBNs)** \[129][^6] stack layers of stochastic variables for feature extraction, yet their training complexity and comparatively lower performance often make them less preferable for emotion recognition tasks.

[^1]: \[124]  R. Fourati, B. Ammar, J. Sanchez-Medina, and A. M. Alimi, "Unsupervised Learning in Reservoir Computing for EEG-Based Emotion Recognition," _IEEE Transactions on Affective Computing_, vol. 13, no. 2, pp. 972-984, Apr.–Jun. 2022, doi: 10.1109/TAFFC.2020.2982143.

[^2]: \[125]  R. Fourati, B. Ammar, Y. Jin, and A. M. Alimi, "EEG Feature Learning with Intrinsic Plasticity Based Deep Echo State Network," in _Proc. 2020 International Joint Conference on Neural Networks (IJCNN)_, Glasgow, UK, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9207464.

[^3]: \[126]  J. Wang, Y. Song, Z. Mao, J. Liu, and Q. Gao, "EEG-Based Emotion Identification Using 1-D Deep Residual Shrinkage Network With Microstate Features," _IEEE Sensors Journal_, vol. 23, no. 5, pp. 5165-5174, Mar. 1, 2023, doi: 10.1109/JSEN.2023.3239507.

[^4]: \[127]  W. Li, C. Fang, Z. Zhu, C. Chen, and A. Song, "Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition," _IEEE Journal of Translational Engineering in Health and Medicine_, vol. 12, pp. 106-118, 2024, doi: 10.1109/JTEHM.2023.3320132.

[^5]: \[128]  W. Alzhrani, M. Doborjeh, Z. Doborjeh, and N. Kasabov, "Emotion Recognition and Understanding Using EEG Data in A Brain-Inspired Spiking Neural Network Architecture," in _Proc. 2021 International Joint Conference on Neural Networks (IJCNN)_, Shenzhen, China, 2021, pp. 1-9, doi: 10.1109/IJCNN52387.2021.9533368.

[^6]: \[129]  D.-K. Jeong, H.-G. Kim, and J. Y. Kim, "Automated Video Classification System Driven by Characteristics of Emotional Human Brainwaves Caused by Audiovisual Stimuli," _IEEE Transactions on Cognitive and Developmental Systems_, vol. 15, no. 2, pp. 651-661, Jun. 2023, doi: 10.1109/TCDS.2022.3179427.
