The brain can be regarded as a network: a connected system where nodes, or units,
represent different specialized regions and links, or connections, represent communication …

EEG is the recording of electrical activities of the brain, usually along the scalp surface,
which are the results of synaptic activations of the brain's neurons. In recent years, it has …

Regularization has been one of the most popular approaches to prevent overfitting in
electroencephalogram (EEG) classification of brain-computer interfaces (BCIs). The …

Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop
seizure detection and prediction algorithms using machine learning (ML) techniques with …

Permutation entropy (PE) is a well-known and fast method extensively used in many
physiological signal processing applications to measure the irregularity of time series …

Electroencephalographic (EEG) recordings are receiving growing attention in the field of
emotion recognition, since they monitor the brain's first response to an external stimulus …

Background and objective Signal segmentation and spike detection are two important
biomedical signal processing applications. Often, non-stationary signals must be segmented …

Recent advancements in signal processing and computerised methods are expected to
underpin the future progress of biomedical research and technology, particularly in …

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A
MATLAB Based Approach presents how machine learning and biomedical signal …

Data is often plagued by noise which encumbers machine learning of clinically useful
biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) …