Neurological disease diagnosis is one of the most significant challenges faced by modern clinics due to the complexity of the nervous system and the overlapping symptoms of various diseases. With nearly 700 identified neurological diseases, including Alzheimer's, Parkinson's, cerebrovascular diseases, multiple sclerosis, neuroinfectious, and neuromuscular diseases, accurate diagnosis remains a critical issue. Current diagnostic methods, such as magnetic resonance imaging (MRI), functional near-infrared spectroscopy (fNIRS), cerebrospinal fluid (CSF) biomarkers, electroencephalographic (EEG) signals, magnetoencephalographic (MEG) signals, positron emission tomography (PET) images, single-photon emission computed tomography (SPECT) images, genetic tests, and neurophysiological tests, provide valuable information but often fall short in diagnostic accuracy. Recent studies have shown that integrating biosignals like EEG, MEG, MRI, fNIRS, and speech with advanced statistical and machine-learning methods can enhance the modeling of cognitive load associated with pathological activity. However, there is still a need for more discriminative pathological measures and improved diagnostic accuracy.

This research topic aims to explore new advances and technologies based on artificial intelligence systems, particularly machine learning and deep learning, for the diagnosis of nervous system diseases using biosignals. The primary objective is to investigate how these advanced computational methods can be leveraged to improve the accuracy and reliability of neurological disease diagnosis. Specific questions to be addressed include: How can machine learning models be optimized for analyzing biosignals? What are the most effective deep learning architectures for detecting specific neurological conditions? How can these technologies be integrated into clinical practice to provide real-time diagnostic support?

To gather further insights into the application of machine learning and deep learning in neurological disease diagnosis, we welcome articles addressing, but not limited to, the following themes:
- Classical machine learning approaches (e.g., decision trees, support-vector machines, k-nearest neighbors)
- Deep learning techniques (e.g., convolutional neural networks, long short-term memory networks, autoencoders)
- Analysis of various biosignals (e.g., EEG, EMG, ECG, MEG, fNIRS, speech, evoked potentials)
- Integration of multimodal biosignal data for comprehensive diagnostic models
- Development of real-time diagnostic tools and systems
- Case studies and clinical trials demonstrating the efficacy of AI-based diagnostic methods
- Ethical considerations and challenges in implementing AI for neurological disease diagnosis

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AI-driven diagnostic enhancements for neurological diseases using multimodal biosignals