Advanced Time Series and Computational Methods in Biological Signal Processing

Dear Colleagues,

The Special Issue “Advanced Time Series and Computational Methods in Biological Signal Processing” aims to address the growing intersection of time series analysis, deep learning, and their transformative applications in biological signal processing and affective computing. With the increasing prevalence of wearable devices, biosensors, and healthcare technologies, vast amounts of physiological and behavioral data are being collected in real time. These data provide unprecedented opportunities to analyze and gain a deeper understanding of human states and emotions, offering transformative potential in areas such as mental health monitoring, personalized medicine, adaptive learning, and human–computer interaction.

Despite the rapid progress in this field, significant challenges remain. Biological signals, such as EEG, ECG, and PPG, are often noisy, complex, and non-stationary, requiring robust analytical methods capable of capturing temporal dependencies and underlying patterns. Traditional time series models often struggle with such complexities, while deep learning models, though powerful, are often computationally intensive and lack interpretability. Furthermore, the fusion of multimodal signals, essential for comprehensive understanding, presents additional challenges related to feature alignment, missing data, and modality-specific noise. Addressing these issues requires innovative models, efficient algorithms, and a collaborative approach that connects theoretical advancements with real-world applications.

This Special Issue provides a platform for researchers and practitioners to share innovative contributions that push the boundaries of time series analysis and deep learning for biological signal processing and affective computing. We invite high-quality submissions that address areas of research, including but not limited to, the following:

1.  Advanced Time Series Models:

• Innovative methodologies for modeling and analyzing complex physiological signals.
• Temporal feature extraction and representation learning in biological data.
• Novel frameworks for real-time signal forecasting and anomaly detection.

2.  Deep Learning for Biological Signal Processing:

• Interpretable deep learning architectures for EEG, ECG, and multimodal data.
• Multi-scale and hybrid models combining classical techniques with neural networks.
• Applications of attention mechanisms, graph neural networks, and transformers.

3.  Affective Computing and Emotion Recognition:

• Algorithms for emotion recognition and regulation based on physiological data.
• Personalized models for tracking and predicting affective states in real time.
• Use of deep learning for non-invasive monitoring and intervention in mental health.

4.  Efficient and Scalable Solutions:

• Lightweight models for resource-constrained environments (e.g., mobile devices).
• Compression techniques like low-rank approximations, pruning, and quantization.
• Privacy-preserving and federated learning methods for sensitive health data.

5.  Real-world Applications and Emerging Topics:

• Cross-disciplinary case studies demonstrating practical applications in healthcare, education, and smart environments.
• Synthetic data generation for training models in data-scarce settings.
• Creation of new benchmarks, datasets, and tools to facilitate future research.

Through this Special Issue, we aim to advance the theoretical foundations and practical applications of time series models and deep learning in biological signal processing and affective computing. By fostering collaboration among experts in artificial intelligence, neuroscience, medicine, and psychology, we hope to address pressing societal challenges and improve human well-being through innovative technological solutions.

Dr. Ziyu Jia
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• time series analysis
• deep learning algorithms
• biological signal processing
• affective computing
• physiological data modeling
• multimodal data fusion
• emotion recognition
• computational neuroscience
• mathematical modeling
• real-time signal processing