Explainable AI Methods for Improving Cross-Session Generalization in EEG-based Brain-Computer Interfaces

Core Concepts

Exploiting explanations from XAI methods can help locate and transform relevant input features to build EEG-based BCI systems that generalize better across different data distributions, such as across recording sessions.

Abstract

This paper investigates the use of various XAI methods to explain the decisions of machine learning models trained on EEG data for emotion recognition tasks. The key findings are:

Among the tested XAI methods, LRP, Integrated Gradients, and DeepLIFT produced more reliable explanations compared to Saliency and Guided Backpropagation, especially in the cross-session setting.

Counterintuitively, the XAI explanations seem more reliable for cross-session data than for data from the same session used for training. This is likely due to the trained classifier being more robust to data from the same session.

While the XAI methods can identify relevant features/channels/bands for individual input samples, they struggle to generalize and identify a common set of relevant components across the dataset. Using the average relevance from the training data does not lead to the same steep performance drop as using the effective relevance.

Further analysis shows that the most relevant components identified by the XAI methods are indeed discriminative on their own. However, the average relevance from training data is not as effective, suggesting the need to better leverage XAI explanations to build more generalizable BCI systems.

The paper concludes that exploiting XAI explanations is a promising direction to mitigate the dataset shift problem in EEG-based BCI, but more work is needed to effectively transfer the relevant components across different data distributions.

Stats

The SEED dataset consists of EEG signals recorded from 15 subjects stimulated by 15 film clips to induce negative, neutral and positive emotions.

Quotes

None

Key Insights Distilled From

Toward the application of XAI methods in EEG-based systems

by Andr... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2210.06554.pdf

Deeper Inquiries

How can the XAI methods be further improved to better identify a common set of relevant EEG components that generalize across different subjects and recording sessions?

To enhance XAI methods in identifying a common set of relevant EEG components that generalize across subjects and recording sessions, several improvements can be implemented:

Incorporating Domain Knowledge: Integrating domain-specific knowledge into the XAI algorithms can help in identifying relevant EEG components more accurately. By leveraging insights from neuroscience and signal processing, the XAI methods can be fine-tuned to focus on specific EEG features that are known to be crucial for emotion recognition.

Ensemble Approaches: Utilizing ensemble methods that combine multiple XAI techniques can provide a more comprehensive and robust analysis of EEG data. By aggregating explanations from different XAI models, a consensus on the most relevant components can be reached, improving generalization across subjects and sessions.

Dynamic Feature Selection: Implementing dynamic feature selection mechanisms that adapt to the changing nature of EEG signals can improve the identification of relevant components. By considering the temporal dynamics and non-stationarity of EEG data, XAI methods can prioritize features that are consistently important across different recording sessions.

Interpretable Model Architectures: Developing interpretable neural network architectures that inherently provide insights into feature importance can enhance the performance of XAI methods. Models designed with transparency in mind can facilitate the identification of relevant EEG components that generalize well across diverse datasets.

Transfer Learning Techniques: Leveraging transfer learning approaches can enable XAI methods to transfer knowledge from one subject or session to another, facilitating the identification of common relevant components. By pre-training XAI models on a diverse set of EEG data, the generalization capabilities can be improved.

How can the insights from this work on XAI-guided feature selection be extended to other types of biosignals beyond EEG to improve cross-domain generalization in biomedical applications?

The insights gained from XAI-guided feature selection in EEG data can be extended to other biosignals in biomedical applications through the following strategies:

Feature Engineering Techniques: Applying similar XAI-guided feature selection methodologies to other biosignals, such as ECG, EMG, or fNIRS, can help in identifying relevant features for classification tasks. By interpreting the model decisions using XAI methods, important features in different biosignals can be highlighted.

Model Agnostic Approaches: Utilizing model-agnostic XAI techniques that are not specific to EEG data can enable the interpretation of various biosignals. Techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) can be adapted to different types of biosignals for feature selection and explanation.

Domain-Specific Adaptation: Tailoring XAI-guided feature selection methods to the unique characteristics of each biosignal can enhance cross-domain generalization. Understanding the specific properties and patterns of different biosignals is crucial for developing effective XAI strategies for feature identification.

Collaborative Research: Collaborating with domain experts in specific biomedical fields can provide valuable insights for extending XAI-guided feature selection to diverse biosignals. By combining expertise in signal processing, physiology, and machine learning, comprehensive approaches for feature selection can be developed.

Benchmarking and Validation: Conducting rigorous benchmarking and validation studies across multiple biosignals can validate the effectiveness of XAI-guided feature selection methods. By comparing the performance of these methods on different types of biosignals, their generalizability and robustness can be assessed.

How can the insights from this work on XAI-guided feature selection be extended to other types of biosignals beyond EEG to improve cross-domain generalization in biomedical applications?

The insights gained from this work on XAI-guided feature selection in EEG data can be extended to other biosignals in biomedical applications through the following strategies:

Feature Engineering Techniques: Applying similar XAI-guided feature selection methodologies to other biosignals, such as ECG, EMG, or fNIRS, can help in identifying relevant features for classification tasks. By interpreting the model decisions using XAI methods, important features in different biosignals can be highlighted.

Model Agnostic Approaches: Utilizing model-agnostic XAI techniques that are not specific to EEG data can enable the interpretation of various biosignals. Techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) can be adapted to different types of biosignals for feature selection and explanation.

Domain-Specific Adaptation: Tailoring XAI-guided feature selection methods to the unique characteristics of each biosignal can enhance cross-domain generalization. Understanding the specific properties and patterns of different biosignals is crucial for developing effective XAI strategies for feature identification.

Collaborative Research: Collaborating with domain experts in specific biomedical fields can provide valuable insights for extending XAI-guided feature selection to diverse biosignals. By combining expertise in signal processing, physiology, and machine learning, comprehensive approaches for feature selection can be developed.

Benchmarking and Validation: Conducting rigorous benchmarking and validation studies across multiple biosignals can validate the effectiveness of XAI-guided feature selection methods. By comparing the performance of these methods on different types of biosignals, their generalizability and robustness can be assessed.