A stroke is a medical emergency also known as a cerebrovascular accident which causes temporary or permanent behavioral disorder in humans. The stroke is classified as ischemic-stroke and hemorrhagic-stroke and early diagnosis and treatment is necessary to reduce its impact.

Due to a variety of causes, the incidence rate of stroke (ischemic/hemorrhagic) has increased rapidly. Hence, automated diagnostic schemes are widely preferred to evaluate the bio-signaling/bio-imaging data collected from the patient. Partial or complete digital healthcare supports accurate and quick data acquisition, assessment, and report generation to forecast the disease condition.

This Special Issue aims to discuss the various advancements employed in the collection and evaluation of bio-signaling (EEG) and bio-imaging (MRI) data to predict the condition of the brain. This Special Issue aims to assemble original research describing methodologies considered to detect stroke (ischemic/hemorrhagic) using single/multichannel EEG and brain MRI of multiple modalities. This Special Issue also welcomes review articles related to stroke detection, assessment, and treatment.

Potential topics include but are not limited to the following:

• Body area network for EEG collection
• EEG examination with 1D and converted 2D data
• Stroke data preservation and Internet of Things (IoT)-based sharing
• 3D MRI processing for stroke lesion segmentation
• Multi-modality fusion for stroke detection
• Classification of EEG and MRI using binary and multi-class classifiers
• Correlating bio-signals and bio-images
• Stroke prediction model development
• In-house and remote patient behavioral monitoring