Abstract Electroencephalogram (EEG)-based Brain–Computer Interfaces (BCIs) build a
communication path between human brain and external devices. Among EEG-based BCI …

Despite the excellent capabilities of machine learning algorithms, their performance
deteriorates when the distribution of test data differs from the distribution of training data. In …

Determining the support patterns of underground mines is challenging due to the complex
structure of the surrounding rock. To address the low accuracies inherent to empirical …

Automated clinical EEG analysis using machine learning (ML) methods is a growing EEG
research area. Previous studies on binary EEG pathology decoding have mainly used the …

Electroencephalography (EEG) decoding is a challenging task due to the limited availability
of labelled data. While transfer learning is a promising technique to address this challenge, it …

The conversion of brain activity into text using electroencephalography (EEG) has gained
significant traction in recent years. Many researchers are working to develop new models to …

In many machine learning applications on signals and biomedical data, especially
electroencephalogram (EEG), one major challenge is the variability of the data across …

Machine Learning (ML) has revolutionized various domains, offering predictive capabilities
in several areas. However, with the increasing accessibility of ML tools, many practitioners …

This study examines the smoking patterns of youth across various states and union
territories of India using the Global Youth Tobacco Survey (GYTS) dataset. The analysis …

In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers
for specific mental tasks is critical for BCI performance. The classifiers are developed by …