Dr Andrei Alexandrov discusses his experience implementing point-of-care EEG equipped with artificial intelligence. As neurologists, our responsibility goes beyond interpreting electroencephalograms ...

Abstract: Automated sleep staging based on EEG signal analysis provides an important quantitative tool to assist neurologists and sleep specialists in the diagnosis and monitoring of sleep disorders ...

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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: Electroencephalography (EEG) can provide objective neural marker for assessing the level of consciousness of patients with disorders of consciousness (DoC), but current research mainly ...

Introduction: Brain-computer interfaces (BCIs) leverage EEG signal processing to enable human-machine communication and have broad application potential. However, existing deep learning-based BCI ...

Abstract: The proposed study aimed to discover the development and optimization of an innovative EEG feature extraction technique, namely the S-Transform, for emotion recognition. In this regard, a ...

Design a lightweight machine-learning pipeline that analyzes single-channel frontal EEG data (Fp1/Fp2) and accurately detects driver drowsiness in real-time. 50 Hz IIR notch filter + 0.5–30 Hz ...