Stay informed on the latest advancements in EEG, biosignals, and physiological signal research. Our blog offers valuable insights and practical knowledge for researchers, clinicians, and enthusiasts alike. Feel free to browse our posts and discover new perspectives in the field!
Electroencephalography (EEG) measures brain activity by detecting electrical currents from neurons on the scalp. This non-invasive technique, pioneered by Hans Berger in 1924, is vital for diagnosing neurological conditions like epilepsy, stroke, and sleep disorders. EEG offers unparalleled temporal resolution, allowing real-time monitoring of brain activity, and can be safely performed by trained professionals in various settings.
Event Related Potentials (ERPs) are brain responses triggered by stimuli, a fundamental tool in neuroscience and psychology. To visualize these subtle electrophysiological signals using EEG, noise must be minimized, often by averaging data from multiple trials. The P300, a well-studied ERP, is a positive waveform emerging around 300 milliseconds after an unexpected event, commonly elicited using the oddball paradigm where rare "deviant" stimuli are interspersed among frequent "default" ones.
Steady-State Visually Evoked Potentials (SSVEPs) are brain signals that synchronize with flickering visual stimuli, making them a popular and reliable tool for Brain-Computer Interfaces (BCIs). By detecting which flickering frequency matches a user's neural activity, SSVEP-based BCIs can enable control over external systems with high accuracy and minimal training. This post delves into SSVEPs, illustrating their application with examples of online and offline classifiers developed using the Mentalab Explore system, freely available for researchers to build upon.
Explore how the Psychological Neuroscience Lab at the University of Minho, in collaboration with University Hospital Centre São João, is using Mentalab Explore to study the impact of early tactile experiences on infant development during the COVID-19 pandemic. Their research involves recording babies' neural responses to touch using EEG, aiming to understand how touch influences well-being and to inform public policy.
In EEG research, noise refers to any unwanted signals detected by sensors, and maximizing the signal-to-noise ratio is crucial for accurate results. This involves careful experimental design to minimize artifacts and employing various mathematical techniques to clean data post-recording.
