Literature study of the EOG artifact problem in EEG data, and a review of possible machine learning solutions. SOBI_implementation_doc: Documentation of implementation and validation of the SOBI ...

Electroencephalography (EEG) has emerged as a non-invasive tool to capture brain activity and facilitate the early detection of ASD using machine learning techniques. However, attaining high accuracy ...

In this study, we used electroencephalogram (EEG) data to extract the features of EEG alpha interhemispheric asymmetry, activity, and mobility, combined with machine learning algorithms, to explore ...

and videos around the car have been gathered. Thanks to this multimodal approach, the present study aimed at: • Validating the machine-learning approach developed by the authors also in automotive ...

The objectives of this study is to develop a robust and accurate machine learning algorithm for AUD detection using EC matrices derived from resting-state EEG signals. This paper employs PDC adjacency ...

It first introduces the history, theories, and flowchart of EEG‐based affective computing, and popular public datasets. Next, it describes commonly used features and machine learning algorithms, ...

"SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG." In Proceedings of the 36th Conference on Neural Information ... In Proceedings of the 39th ...

A brain-computer interface, surgically placed in a research participant with tetraplegia, paralysis in all four limbs, ...

We use neural imaging (fMRI, EEG, MEG) to study the sequential structure of complex tasks like mathematical problem solving and video games. Machine learning techniques combine imaging data and ...