博灵脑机BLBCI - 绘梦仪Dreamgear - Beijing Language and Culture University Cognitive Intelligence Institute
BLBCI focuses on research in cognitive psychology, artificial intelligence, and language processing, providing resources for academic research and collaboration.
Detalyadong Panimula
The BLBCI is dedicated to advancing cognitive intelligence research through interdisciplinary work in cognitive psychology, neuroscience, and artificial intelligence. It offers educational resources, research programs, and fosters collaboration in the field of cognitive science. The Dreamgear Dream Painter uses 10 highly sensitive brain-computer signal channels to accurately capture brain neural signals. It is equipped with Dreamer, the world's exclusive brain-computer sleep and dream model jointly developed with Zhejiang University for many years, which changes dream fragments from blurry to clear, and from fleeting to permanent collection. Just as the movie 'Inception' opened the door to subconscious exploration, Dreamgear also allows us to take the first step into the dream world. It not only preserves those unique dream stories, but also creates the possibility of deeper dream interaction and creation in the future. Perhaps one day, we can really travel freely in the vast world of the subconscious like the characters in 'Inception', and Dreamgear is the starting point of this fantasy journey.
Higit paAI
Clearmind | AI Therapy for All
Clearmind is a personalized AI therapy platform designed to measure and elevate emotional health through real-time, hyper-personalized meditation and mood-based tools.
electronic media datasets - Mental Health Datasets
An evolving list of electronic media datasets used to model mental health status. This repository curates a variety of datasets from different sources, including social media platforms, online forums, and academic studies, to support research in mental health modeling and AI applications.
Depression: Twitter Dataset + Feature Extraction
This dataset contains 20,000 labelled English tweets of depressed and non-depressed users. The data is collected using the Twitter API and includes feature extraction techniques such as topic modelling and emoji sentiment analysis. It is designed for mental health classification at the tweet level.