Explore how machine learning, EEG data, and high-performance computing can help detect signs of consciousness.

Electroencephalography (EEG) has emerged as a valuable non‐invasive tool for examining neural dynamics and recognising patterns associated with depressive disorders. Recent advances in deep learning ...

An overview of attention detection using EEG signals, which includes six steps: an experimental paradigm design, in which the task and the stimuli are defined and presented to the subjects; EEG data ...

The first patenting from Encephalogix Inc. details its development of platform that uses machine learning and AI to analyze EEG data that is typically ignored.

Recent advances in the integration of electroencephalography (EEG) with machine learning techniques have provided promising avenues for the early detection and monitoring of Alcohol Use Disorder (AUD) ...

Science and data are interwoven in many ways. The scientific method has lent a good part of its overall approach and practices to data-driven analytics, software development, and data science. Now ...

Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language ...

Data models and query languages are admittedly somewhat dry topics for people who are not in the inner circle of connoisseurs. Although graph data models and query languages are no exception to that ...

Graph database developer Neo4j Inc. is upping its machine learning game today with a new release of Neo4j for Graph Data Science framework that leverages deep learning and graph convolutional neural ...