Machine-Learning-Based Diagnostics of EEG Pathology.

Link – Machine-Learning-Based Diagnostics of EEG Pathology. Abstract Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both. For a I) […]

Convolutional neural network for seizures.

Link – Convolutional neural network for detection and classification of seizures in clinical data. Abstract Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients […]

Semantic-hierarchical model classification of eeg

Link – Semantic-hierarchical model improves classification of spoken-word evoked electrocorticography. Abstract Classification of spoken word-evoked potentials is useful for both neuroscientific and clinical applications including brain-computer interfaces (BCIs). By evaluating whether adopting a biology-based structure improves a classifier’s accuracy, we can investigate the importance of such structure in human brain circuitry, and advance BCI performance. […]

Instructor-learner brain coupling with fNIRS

Link – Instructor-learner brain coupling discriminates between instructional approaches and predicts learning. Abstract The neural mechanisms that support naturalistic learning via effective pedagogical approaches remain elusive. Here we used functional near-infrared spectroscopy to measure brain activity from instructor-learner dyads simultaneously during dynamic conceptual learning. Results revealed that brain-to-brain coupling was correlated with learning outcomes, and, […]