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

In this study, researchers developed a deep learning framework to analyse EEG signals from individuals with Alzheimer’s disease, frontotemporal dementia, and cognitively normal controls. The model ...

Summary: New research shows that deep learning can use EEG signals to distinguish Alzheimer’s disease from frontotemporal dementia with high accuracy. By analyzing both the timing and frequency of ...

Start your journey into machine learning with EEG time-series data in this easy-to-follow Python project. Perfect for beginners looking to explore brain signal analysis! #MachineLearning #EEG ...

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

Mind wandering, the shift of attention from an ongoing task to task-unrelated thoughts, is a pervasive cognitive phenomenon often accompanied by detrimental consequences for task performance. While ...

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 ...

This is general info. Click here for the complete wiki and here for a more generic intro to audio data handling ...