Abstract: Decoding motor imagery (MI) from electroencephalogram (EEG) signals is a cornerstone of brain–computer interface (BCI) systems. However, existing methods often face a critical tradeoff ...
Introduction: We present connectivity-based features associated with fibromyalgia, derived from raw EEG data at the sensor level. Methods: These connectivity features were identified through a ...
Dr Andrei Alexandrov discusses his experience implementing point-of-care EEG equipped with artificial intelligence. As neurologists, our responsibility goes beyond interpreting electroencephalograms ...
Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!
Introduction: Resting-state electroencephalogram (EEG) presents a promising biometric modality due to its inherent liveness detection and resistance to spoofing, addressing critical vulnerabilities in ...
A complete brain-computer interface system that connects to your Muse 2 EEG headset and detects your emotional state in real-time using AI. Features automated model training so the AI learns YOUR ...
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 ...
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