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) , 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) 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) , 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) 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.