Improving Cognitive Workload Prediction from fNIRS Data through Class-Aware and Block-Aware Domain Adaptation

Core Concepts

The proposed class-aware and block-aware domain adaptation (CABA-DA) method can effectively minimize the intra-class domain discrepancy and maximize the inter-class domain discrepancy, leading to improved cognitive workload classification performance from fNIRS data across different subjects and sessions.

Abstract

The paper focuses on the problem of classifying cognitive workload levels from functional near-infrared spectroscopy (fNIRS) data. The key insights and contributions are:

Empirical analysis shows that the performance of deep learning models is significantly affected not only by the differences between subjects, but also by the variations within the same subject across different blocks and sessions. This intra-subject variance needs to be addressed.

The authors propose a Class-Aware-Block-Aware Domain Adaptation (CABA-DA) method that explicitly minimizes the intra-class domain discrepancy and maximizes the inter-class domain discrepancy. This helps to align the feature representations across different blocks and subjects.

An MLPMixer-based model is proposed for cognitive load classification, which is better suited for fNIRS data compared to CNN-based models. The MLPMixer architecture avoids the parameter sharing assumption of CNNs that may not hold for brain signal data.

Experiments on three public fNIRS datasets show that the proposed CABA-DA method can improve the performance of both the MLPMixer model and other baseline models like DeepConv and EEGNet. The MLPMixer model with CABA-DA achieves the best classification accuracy across the datasets.

The proposed approach effectively addresses the challenges of high inter-subject and intra-subject variances in fNIRS data, enabling better generalization to new subjects and sessions.

Stats

"The accuracy drops by 8.62% when we change the split approach from trail to subject."
"The accuracy further decreases to 40.44% when the data is split by subject, showing that the model fails to transfer to new subjects."
"The lowest accuracy of 39.92% is obtained when the data is split by block, suggesting that the model cannot handle the intra-variance between blocks of the same task or condition."

Quotes

"The parameter sharing aspect [11] of CNNs is based on the assumption that a valid patch of weights working for one position also works for other regions. However, later studies [12], [13] have shown that this invariance assumption does not always hold."
"Prior fMRI literature on working memory load and selective attention has shown that the middle temporal gyrus is a region involved in the coordination between working memory and directed attention [14]. In addition, human brain is highly interconnected, and some brain regions can work together in cognitive processing."

Key Insights Distilled From

Block-As-Domain Adaptation for Workload Prediction from fNIRS Data

by Jiyang Wang,... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00213.pdf

Deeper Inquiries

How can the proposed CABA-DA approach be extended to handle more complex cognitive tasks beyond the n-back and finger tapping experiments

The proposed CABA-DA approach can be extended to handle more complex cognitive tasks beyond the n-back and finger tapping experiments by incorporating task-specific features and domain adaptation strategies. For more complex tasks, such as decision-making, problem-solving, or emotional processing, the model can be trained on a wider range of cognitive tasks to capture a broader spectrum of cognitive workload levels. Additionally, the CABA-DA approach can be enhanced by incorporating multi-modal data fusion techniques, combining fNIRS data with other neuroimaging modalities like EEG or fMRI to provide a more comprehensive understanding of cognitive processes. By integrating data from multiple sources, the model can learn more robust and generalizable representations of cognitive workload across different tasks and conditions.

What other domain adaptation techniques could be explored to further improve the generalization of the models across subjects and sessions

In addition to the proposed CABA-DA approach, other domain adaptation techniques could be explored to further improve the generalization of the models across subjects and sessions. One approach could be to incorporate adversarial domain adaptation, where a domain discriminator is added to the model to distinguish between source and target domain samples. By minimizing the domain discrepancy, the model can learn domain-invariant features that are more transferable across different subjects and sessions. Another technique is self-supervised domain adaptation, where the model is trained on auxiliary tasks using unlabeled data to learn domain-invariant representations. By leveraging self-supervision, the model can improve its ability to generalize to new subjects and sessions without labeled data. Additionally, meta-learning approaches can be employed to adapt the model to new subjects or tasks with minimal labeled data, enabling faster adaptation and improved performance on unseen data.

What insights from neuroscience research on the functional organization of the brain could be leveraged to design more effective neural network architectures for fNIRS-based cognitive workload prediction

Insights from neuroscience research on the functional organization of the brain can be leveraged to design more effective neural network architectures for fNIRS-based cognitive workload prediction. For example, knowledge about the specific brain regions involved in cognitive tasks can inform the design of task-specific neural network modules that mimic the functional connectivity of the brain. By incorporating domain knowledge about the brain's functional organization, the neural network architecture can be tailored to capture the underlying cognitive processes more accurately. Additionally, insights from studies on brain connectivity patterns can guide the design of attention mechanisms in neural networks, allowing the model to focus on relevant brain regions and features during cognitive workload prediction tasks. By integrating neuroscience principles into the design of neural network architectures, researchers can develop more interpretable and effective models for fNIRS-based cognitive workload prediction.