Over the last few decades, the Brain-Computer Interfaces have been gradually making their
way to the epicenter of scientific interest. Many scientists from all around the world have …

The electroencephalogram (EEG), for measuring the electrophysiological activity of the
brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG …

This review paper provides an integrated perspective of Explainable Artificial Intelligence
techniques applied to Brain-Computer Interfaces. BCIs use predictive models to interpret …

Dementia is an incurable neurodegenerative disease primarily affecting the older
population, for which the World Health Organisation has set to promoting early diagnosis …

Electroencephalogram (EEG) is often used in clinical epilepsy treatment to monitor electrical
signal changes in the brain of patients with epilepsy. With the development of signal …

EEG signal classification is a key technology for EEG signal processing and identification
systems. Dictionary learning has shown excellent performance due to its sparse …

Text sentiment classification is a fundamental sub-area in natural language processing. The
sentiment classification algorithm is highly domain-dependent. For example, the phrase …

Artificial intelligence (AI) is an effective technology for automatic brain tumor MRI image
recognition. The training of an AI model requires a large number of labeled data, but medical …

Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …

Through semi-supervised learning and knowledge inheritance, a novel Takagi-Sugeno-
Kang (TSK) fuzzy system framework is proposed for epilepsy data classification in this study …