Topic: feature extraction

Supports preparation, integration and enhancement of existing scientific community datasets for AI-driven discovery. Work may improve feature extraction, metadata, data pipelines, harmonization and dataset accessibility. Proposals must address governance, security, integrity, community benefit and sustained availability. New primary data collection is outside scope. Impact awards are up to USD 2 million, Flagship awards up to USD 5 million, and Planning grants up to USD 200,000. Full proposals are due 4 November 2026 at 5 pm in the submitting organization’s local time.

Coregraph is a tool under development that allows us to collect high-density data patterns during the administration of classic neuropsychological tests such as the Trail Making Test and Clock Drawing Test. These tests are widely used to evaluate cognitive function and screen for neurodegenerative disorders, but traditional methods of data collection only yield sparse information, such as test completion time or error types. By contrast, the high-density data collected with Coregraph may contribute to a better understanding of the cognitive processes involved in executing these tests. In addit

The application of neuroimaging methods to marketing has recently gained lots of attention. In analyzing consumer behaviors, the inclusion of neuroimaging tools and methods is improving our understanding of consumer’s preferences. Human emotions play a significant role in decision making and critical thinking. Emotion classification using EEG data and machine learning techniques has been on the rise in the recent past. We evaluate different feature extraction techniques, feature selection techniques and propose the optimal set of features and electrodes for emotion recognition.Affective neuros