Machine Learning Techniques for Brain Data Analysis Using EEG, EMG or Image Data
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 1307
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Special Issue Information
Dear Colleagues,
The evolution of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), holds promising leads for unsealing novel horizons in precise diagnosis and disease prediction in the clinical domain. Many recent advancements in machine learning (ML) and deep learning (DL) algorithms are being applied to brain data analysis, particularly in applications related to neurological disorder diagnosis, cognitive state monitoring, brain–computer interfaces, and neuroimage analysis. The primary sources of data for brain data analysis have been extracted from electroencephalogram (EEG), electromyography (EMG), magnetic resonance imaging (MRI), and computed tomography scans (CT Scans). Traditional machine learning (ML) models, such as Support Vector Machines (SVM), Random Forest, and K-Nearest Neighbors (KNN), are primarily used for emotional detection from EEG, movement classification, and pattern recognition in EEG/EMG-based classification. Techniques like PCA and ICA are used for dimensionality reduction. The application of machine learning (ML) and deep learning (DL) transforms brain data analysis by leveraging EEG, EMG, and medical imaging to enhance the diagnosis, treatment, and monitoring of neurological disorders. Several applications based on EEG analysis are focused on epilepsy detection, sleep disorder diagnosis, and cognitive and mental health assessment. Additionally, models developed based on EMG analysis aid in diagnosing neurodegenerative diseases, controlling prosthetic and assistive devices, and detecting muscle fatigue. Other applications of MRI, CT scans, and PET scans help to detect brain tumors and lesions, Alzheimer’s and dementia prediction, stroke detection and prognosis, etc. ML-driven EEG, EMG, and imaging analysis enable early detection, personalized treatments, and real-time monitoring of neurological disorders. The increasing prevalence of neurological disorders and the challenges involved in their diagnosis accentuate the need for early detection and more effective therapeutic measures. These technologies can revolutionize neurological care by enabling faster and more accurate diagnoses, as well as enhanced patient outcomes. EEG, EMG, and image analysis with ML are being daunted by variability in data, noise, and limited labeled datasets. Model generalizability, interpretability, and real-time processing remain significant challenges. Ethical concerns, regulatory approval, and clinician trust are hindering large-scale adoption. Future advancements, including federated learning and multimodal AI, will enhance diagnostic accuracy and patient outcomes.
We welcome research papers related to EEG or EMG data analysis by machine learning methods. These data are particularly important and useful for analyzing brain signals associated with various diseases, as mentioned in the summary. We would appreciate high-quality papers related to these datasets and AI techniques for processing and analyzing such data.
Prof. Dr. Kalpdrum Passi
Prof. Dr. Francesc Pozo
Guest Editors
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Keywords
- electroencephalogram (EEG)
- electromyography (EMG)
- magnetic resonance imaging (MRI)
- computed tomography scan (CT Scan)
- brain tumors and lesions
- Alzheimer’s and dementia prediction
- stroke detection and prognosis
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