Joint Contrastive Learning with Feature Alignment for Improving Cross-Corpus EEG-based Emotion Recognition

Core Concepts

The proposed JCFA model effectively extracts generalizable time-frequency representations of EEG signals through joint contrastive learning, enabling robust cross-corpus emotion recognition.

Abstract

The paper presents a novel Joint Contrastive Learning with Feature Alignment (JCFA) framework for cross-corpus EEG-based emotion recognition. The key highlights are:

The JCFA model operates in two stages:

Pre-training stage: A joint contrastive learning strategy is introduced to characterize generalizable time-frequency representations of EEG signals, without using any labeled data. This extracts robust time-based and frequency-based embeddings and aligns them within a shared latent time-frequency space.
Fine-tuning stage: The pre-trained model is further refined using a small amount of labeled data, incorporating spatial features of brain electrodes via a graph convolutional network.

Extensive experiments on two well-recognized datasets (SEED and SEED-IV) show that JCFA achieves state-of-the-art performance, outperforming the second-best method by an average accuracy increase of 4.09% in cross-corpus EEG-based emotion recognition tasks.

Ablation studies demonstrate the effectiveness of each module in the JCFA framework, highlighting the importance of time-frequency domain contrastive learning with alignment loss in extracting generalizable EEG representations.

Further analysis reveals that increasing the fine-tuning size can effectively improve the model performance, but excessive fine-tuning may introduce additional noise and interfere with the learning process.

Stats

The proposed JCFA model achieves a classification accuracy of 67.53% with a standard deviation of 12.36% for the SEED-IV3 → SEED3 experiment, outperforming the second-best method E2STN by 7.02% in accuracy.
The JCFA model achieves a recognition accuracy of 62.40% with a standard deviation of 7.54% for the SEED3 → SEED-IV3 experiment, surpassing E2STN by 1.16% in accuracy.

Quotes

"The proposed JCFA model achieves state-of-the-art (SOTA) performance, outperforming the second-best method by an average accuracy increase of 4.09% in cross-corpus EEG-based emotion recognition tasks."
"Extensive experimental results on two well-recognized emotional datasets show that the proposed JCFA model achieves state-of-the-art (SOTA) performance, outperforming the second-best method by an average accuracy increase of 4.09% in cross-corpus EEG-based emotion recognition tasks."

Key Insights Distilled From

Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition

by Qile Liu,Zhi... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09559.pdf

Deeper Inquiries

How can the proposed JCFA framework be extended to other physiological signals beyond EEG for cross-corpus emotion recognition

The JCFA framework can be extended to other physiological signals beyond EEG for cross-corpus emotion recognition by adapting the model architecture and training process to suit the characteristics of the new signals. Here are some ways to extend JCFA to other physiological signals:

Feature Extraction: Different physiological signals may require specific feature extraction techniques. For example, for ECG signals, features like R-R intervals or heart rate variability can be extracted. The model can be modified to incorporate these signal-specific features.

Data Preprocessing: Each physiological signal may have unique preprocessing requirements. For example, filtering techniques for EMG signals or artifact removal methods for ECG signals. Adapting the data preprocessing steps in JCFA to suit the new signals is essential.

Model Architecture: The neural network architecture in JCFA can be adjusted to accommodate the characteristics of different physiological signals. For example, for EMG signals, a convolutional neural network (CNN) may be more suitable to capture spatial information.

Loss Functions: The contrastive learning strategy in JCFA can be modified to account for the specific properties of the new signals. Customized loss functions can be designed to optimize the model for different physiological data types.

Fine-tuning Strategies: The fine-tuning stage of JCFA can be tailored to the nuances of the new signals. Incorporating domain-specific knowledge and fine-tuning techniques can enhance the model's performance on diverse physiological data.

By customizing the feature extraction, data preprocessing, model architecture, loss functions, and fine-tuning strategies to the characteristics of other physiological signals, the JCFA framework can be effectively extended for cross-corpus emotion recognition beyond EEG.

What are the potential limitations of the current JCFA model, and how can it be further improved to handle more challenging cross-corpus scenarios

The current JCFA model, while showing promising results in cross-corpus EEG-based emotion recognition, may have some limitations that could be addressed for further improvement:

Generalizability: The model's performance may vary when applied to highly diverse datasets with significant variations in data distribution. To improve generalizability, more robust data augmentation techniques and regularization methods can be incorporated.

Scalability: Handling larger datasets and more complex cross-corpus scenarios may pose challenges in terms of computational efficiency and scalability. Implementing distributed computing or parallel processing techniques can help address scalability issues.

Interpretability: The interpretability of the model's decisions and feature representations can be enhanced. Incorporating explainable AI techniques such as attention mechanisms or feature visualization methods can provide insights into the model's decision-making process.

Data Imbalance: Addressing class imbalance in the datasets used for training and testing is crucial. Techniques like oversampling, undersampling, or class-weighted loss functions can help mitigate the effects of imbalanced data distributions.

Transfer Learning: Leveraging transfer learning from pre-trained models on related tasks or domains can further enhance the model's performance in handling more challenging cross-corpus scenarios.

By addressing these limitations through improved generalizability, scalability, interpretability, handling data imbalance, and leveraging transfer learning, the JCFA model can be further improved to handle more complex cross-corpus scenarios effectively.

Given the promising results in cross-corpus EEG-based emotion recognition, how can the JCFA model be applied to other brain-computer interface applications, such as mental state monitoring or neural decoding

The success of the JCFA model in cross-corpus EEG-based emotion recognition opens up opportunities for its application in other brain-computer interface (BCI) applications, such as mental state monitoring or neural decoding. Here are some ways the JCFA model can be applied to these areas:

Mental State Monitoring: The JCFA model can be adapted to classify mental states based on EEG signals, such as stress levels, cognitive load, or attention levels. By training the model on labeled EEG data corresponding to different mental states, it can accurately classify and monitor an individual's mental state in real-time.

Neural Decoding: In the field of neural decoding, the JCFA model can be used to decode neural activity patterns from EEG signals to understand cognitive processes or intentions. By training the model on labeled EEG data associated with specific neural activities, it can predict and decode the underlying neural patterns.

BCI Applications: The JCFA model can be integrated into BCI systems for applications like neurofeedback training, brain-controlled interfaces, or assistive technologies. By leveraging the model's ability to extract meaningful features from EEG signals, it can enhance the performance and usability of BCI systems for various applications.

Real-time Monitoring: The JCFA model can be deployed for real-time monitoring of brain activity in applications like driver fatigue detection, mental health assessment, or cognitive performance evaluation. By continuously analyzing EEG signals and detecting patterns associated with specific states, the model can provide valuable insights for monitoring and intervention.

By applying the JCFA model to these BCI applications, it can contribute to advancements in mental state monitoring, neural decoding, BCI technology, and real-time brain activity analysis for various practical and research purposes.