Source localization in EEG represents a high dimensional inverse problem, which is severely ill-posed by nature. Fortunately, sparsity constraints have come into rescue as it helps solving the ill-posed problems when the signal is sparse. When the signal has a structure such as block structure, consideration of block sparsity produces better results. Knowing sparse Bayesian learning is an impor...

Electrophysiological observation plays a major role in epilepsy evaluation. However, human interpretation of brain signals is subjective and prone to misdiagnosis. Automating this process, especially seizure detection relying on scalpbased Electroencephalography (EEG) and intracranial EEG, has been the focus of research over recent decades. Nevertheless, its numerous challenges have inhibited a...

Background: Learning disabilities (LDs) are diagnosed in children impaired in the academic skills of reading, writing, and/or mathematics. Children with LDs usually exhibit a slower resting-state electroencephalogram (EEG), corresponding to a neurodevelopmental lag. The present study aimed to investigate the effectiveness of neurofeedback treatment on working memory and processing speed among g...

Recently, the advances in passive brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have shed light real-world neuromonitoring technologies. However, human variability EEG activities hinders development of practical applications EEG-based BCI. To tackle this problem, many transfer-learning techniques perform supervised calibration. This kind calibration approach requires task...

We describe a spatio-temporal linear discriminator for single-trial classification of multi-channel electroencephalography (EEG). No prior information about the characteristics of the neural activity is required, i.e., the algorithm requires no knowledge about the timing and spatial distribution of the evoked responses. The algorithm finds a temporal delay/window onset time for each EEG channel...