Enhancing Robustness of EEG-based Emotion Recognition Using an Inception-based Feature Generator and Two-sided Perturbation Model

Concepts de base

A novel deep learning approach called Inception feature generator and two-sided perturbation (INC-TSP) is proposed to extract effective features from EEG data and learn a robust model against adversarial attacks for subject-independent emotion recognition.

Résumé

The paper presents a novel deep learning approach called Inception feature generator and two-sided perturbation (INC-TSP) for robust EEG-based emotion recognition.

The key highlights are:

1. Inception-based Feature Generator:

   • The Inception module is integrated with a CNN backbone to perform multiscale analysis of the spatial, temporal, and spectral characteristics of EEG data.
   • This enables effective feature extraction from the EEG signals.

2. Robust Generalization:

   • Two-sided perturbation (TSP) is employed as a defensive mechanism against input perturbations and adversarial attacks.
   • TSP applies worst-case perturbations to both the input data and the model's weights, reinforcing the model's elasticity.

3. Evaluation:

   • The proposed INC-TSP approach is evaluated on the SEED dataset for a subject-independent three-class emotion recognition scenario.
   • INC-TSP demonstrates robust performance, maintaining high accuracy and F1-scores under various adversarial attack scenarios.
   • Ablation studies and comparisons with previous works further validate the effectiveness of the proposed approach.

The results show that INC-TSP can effectively counter input perturbations and adversarial attacks, while maintaining accurate emotion recognition, making it a promising solution for robust BCI applications.

Stats

The EEG signals were recorded using the international 10-20 system with 62 channels, downsampled from 1000 Hz to 200 Hz, and a band-pass filter with a frequency range of 0.5-70 Hz was applied.
Differential entropy (DE) features were calculated every 1-second with no overlap in five frequency subbands: delta, theta, alpha, beta, and gamma.

Citations

"To overcome such attacks, we employ adversarial training (AT) with adversarial weight perturbation as a defensive scheme inspired by [16]. This approach involves the use of two-sided perturbation (TSP), which applies worst-case perturbations to both the input data and the model's weights."
"Results indicate that the INC-TSP model consistently achieves robust accuracy and F1-scores across various threat models and adversarial attack scenarios which shows its efficacy in countering perturbations."

Idées clés tirées de

Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model

by Shadi Sartip... à arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15373.pdf

Questions plus approfondies

How can the proposed INC-TSP approach be extended to handle more complex emotion recognition tasks, such as fine-grained emotion classification or continuous emotion tracking

The proposed INC-TSP approach can be extended to handle more complex emotion recognition tasks by incorporating advanced deep learning techniques and expanding the dataset for training. To address fine-grained emotion classification, the model can be trained on a more diverse set of emotional states, allowing it to differentiate between subtle variations in emotions. This can involve collecting data on a wider range of emotions beyond the basic categories like happiness, sadness, and neutrality. Additionally, incorporating recurrent neural networks (RNNs) or attention mechanisms can help capture temporal dependencies and nuances in emotional expressions over time, enabling continuous emotion tracking. By leveraging more sophisticated architectures and richer emotional datasets, the model can learn to recognize and track a broader spectrum of emotions with higher granularity and accuracy.

What are the potential limitations of the two-sided perturbation technique, and how can it be further improved to enhance the robustness of the model against a wider range of adversarial attacks

While the two-sided perturbation technique used in the INC-TSP model enhances robustness against adversarial attacks, it may have limitations in handling certain types of attacks or scenarios. One potential limitation is the computational overhead associated with applying perturbations to both input data and model weights, which could impact real-time applications or scalability to larger datasets. To address this, optimization techniques can be explored to streamline the perturbation process and reduce computational complexity without compromising defense effectiveness. Additionally, the model's sensitivity to the choice of hyperparameters, such as the perturbation size and learning rate, could be a limitation. Fine-tuning these hyperparameters through automated methods like grid search or Bayesian optimization can help optimize the model's robustness across different attack scenarios. Furthermore, exploring ensemble methods that combine multiple perturbation strategies or incorporating domain-specific knowledge into the perturbation process can enhance the model's resilience to a wider range of adversarial attacks.

Given the importance of interpretability in BCI applications, how can the feature extraction and decision-making process of the INC-TSP model be made more transparent and explainable to users

To enhance the interpretability of the feature extraction and decision-making process in the INC-TSP model for BCI applications, several strategies can be employed. Firstly, incorporating visualization techniques such as saliency maps or activation maximization can help highlight the regions of input data that contribute most to the model's decisions, providing users with insights into the features driving the emotion recognition process. Additionally, feature importance analysis methods like SHAP (SHapley Additive exPlanations) can be utilized to quantify the impact of each input feature on the model's predictions, aiding in understanding the model's decision logic. Moreover, integrating domain knowledge into the model architecture, such as incorporating domain-specific EEG signal characteristics or emotion-related biomarkers, can make the feature extraction process more transparent and aligned with existing research in the field. By combining these techniques and ensuring the model's interpretability aligns with user expectations and domain requirements, the INC-TSP model can provide users with actionable insights and a deeper understanding of the emotion recognition process in BCI applications.