Introduction: Physiological signals offer a significant advantage in the field of emotion recognition due to their objective nature, as they are less susceptible to volitional control and thus provide ...
Overview of our analysis pipeline including constructing three types of RDMs and conducting comparisons between them. We computed RDMs from three sources: neural data (EEG), hypothesized object ...
Abstract: Signal processing is crucial for satisfying the high data rate requirements of future sixth-generation (6G) wireless networks. However, the rapid growth of wireless networks has brought ...
Abstract: Graph Signal Processing (GSP), an emerging field, provides a flexible framework to model and analyze Electroencephalogram (EEG) sensor data that exhibit intricate relationships and ...
Firefly further enhanced the platform with Nvidia GPU acceleration, achieving a 60–80% improvement in processing speeds. The advancement highlights Nvidia’s growing footprint in healthcare and life ...
Electroencephalogram (EEG) signal analysis plays a vital role in diagnosing and monitoring alcoholism, where accurate classification of individuals into alcoholic and control groups is essential.
• Amplitude-integrated electroencephalography (aEEG) may not be sufficiently accurate to identify babies with seizures and also individual seizure episodes in a baby. • Treatment based only on aEEG ...
The findings are valuable, given that they highlight the flexible and future-oriented nature of working memory. However, the evidence for the claims about context/color generalization, behavioural ...
Startup RelationalAI Inc. today introduced new features for its software that will enable companies to analyze their data more efficiently. The capabilities debuted at the annual Snowflake Summit in ...
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