Sleep is fundamental to our well-being, yet understanding its trends can be complex. EEG offers unique insights into brain activity during sleep, helping us uncover factors contributing to restlessness and fatigue. Mobile EEG devices, like Mentalab Explore, are revolutionizing sleep research by enabling comfortable, at-home recordings that capture comprehensive sleep data, rivaling traditional in-lab polysomnography.
Prepare to be amazed! Mentalab's collaborator, Dr. Stephen Whitmarsh, is pioneering a unique application of EEG by transforming brain waves into musical sound waves using EEGsynth, an open-source Python codebase. We're excited to share insights from Dr. Whitmarsh himself on how he integrated Mentalab Explore with EEGsynth to achieve this innovative fusion of neuroscience and music.
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.
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.
Choosing between wet and dry EEG electrodes depends on your research needs, with dry electrodes gaining popularity due to the rise of mobile EEG. While wet electrodes offer slightly superior signal quality and stability for stationary or shorter recordings, dry electrodes provide unparalleled convenience and faster setup times, especially for high-density systems or mobile applications where gel is impractical. The decision involves balancing data quality with ease of use, with ongoing advancements in both electrode technology and signal processing techniques continually narrowing the gap.