The Context Hierarchical Contrastive Learning for Time Series in Frequency Domain

Wang, Minghui; Liu, Jian-wei

doi:10.1007/978-981-99-1639-9_4

Cited by 3 publications

(4 citation statements)

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“…The subject-independent setting, especially in the context of evaluating unseen patients or subjects, is crucial for developing a diagnostic or medical aid that is both resilient and universally applicable [15]. Each subject exhibits unique characteristics and cognitive activities, based on which certain consistent brain activity patterns may be identified and generalized.…”

Section: Subject-independent Resultsmentioning

confidence: 99%

“…The challenges in subject-independent settings come from the unique noisy characteristics of each subject. Even when sharing the same class label, subjects might exhibit different data distributions [15,16]. The higher accuracy and F1 score observed in the subject-dependent and subject-semidependent settings can be attributed to potential "information leakage", where the model learns specific subjects' distributions during training.…”

Section: Discussionmentioning

confidence: 99%

“…However, due to the low information-to-noise ratio, achieving generalization to unseen subjects is quite challenging. Each subject has unique characteristics and patterns that may obscure the acquisition of general features across subjects, and the distribution of data for each subject can be markedly different even for those with the same label [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…Our approach includes analyses in various settings: subject-dependent, subject-semidependent, and subject-independent. This multifaceted strategy allows for a more flexible and versatile application of the models, empowering them to learn and adapt to a broader spectrum of neural data and mental states irrespective of the subjects' previous interactions with the model [15]. We evaluated our EF-Net model on the EEG-fNIRS WG dataset from [17], achieving impressive F1 scores of 99.36% in subject-dependent analyses, 98.31% in subjectsemidependent analyses, and 65.05% in subject-independent analyses.…”

Section: Introductionmentioning

confidence: 99%

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EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN

Arif,

Wang,

Yin

et al. 2024

Sensors

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Analysis of brain signals is essential to the study of mental states and various neurological conditions. The two most prevalent noninvasive signals for measuring brain activities are electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG, characterized by its higher sampling frequency, captures more temporal features, while fNIRS, with a greater number of channels, provides richer spatial information. Although a few previous studies have explored the use of multimodal deep-learning models to analyze brain activity for both EEG and fNIRS, subject-independent training–testing split analysis remains underexplored. The results of the subject-independent setting directly show the model’s ability on unseen subjects, which is crucial for real-world applications. In this paper, we introduce EF-Net, a new CNN-based multimodal deep-learning model. We evaluate EF-Net on an EEG-fNIRS word generation (WG) dataset on the mental state recognition task, primarily focusing on the subject-independent setting. For completeness, we report results in the subject-dependent and subject-semidependent settings as well. We compare our model with five baseline approaches, including three traditional machine learning methods and two deep learning methods. EF-Net demonstrates superior performance in both accuracy and F1 score, surpassing these baselines. Our model achieves F1 scores of 99.36%, 98.31%, and 65.05% in the subject-dependent, subject-semidependent, and subject-independent settings, respectively, surpassing the best baseline F1 scores by 1.83%, 4.34%, and 2.13% These results highlight EF-Net’s capability to effectively learn and interpret mental states and brain activity across different and unseen subjects.

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Section: Subject-independent Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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