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.
Low-density EEG systems are gaining popularity, offering significant advantages for research, even complementing existing high-density setups. While high-density systems are still the gold standard for detailed source localization, mobile low-density devices provide comparable data quality for many paradigms, enable parallel data collection from multiple subjects, and offer substantial cost savings. Their portability also allows for ecologically valid research outside the lab and helps address inequities in traditional EEG studies by reaching underrepresented populations.
Reducing noise is paramount in EEG research, especially when the signal-to-noise ratio is low, as high noise levels can obscure significant findings and hinder publication. While traditional noise reduction methods are often cumbersome, Steady-State Visually Evoked Potentials (SSVEPs) offer a powerful alternative to enhance signal visibility.
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.
While 32 EEG channels have historically been the "gold standard" for research, new findings suggest that fewer channels can achieve comparable results with proper pre-processing techniques. Using fewer channels significantly reduces setup time, material costs, and participant discomfort, making research more efficient and accessible.
The COVID-19 pandemic has significantly impacted scientific research, presenting challenges like financial uncertainty, limited lab access, and difficulties in participant recruitment, especially for at-risk groups. Mobile EEG solutions, such as Mentalab Explore, offer a powerful way to overcome these hurdles by enabling remote data collection, reducing costs, and facilitating adherence to safety guidelines, thus allowing research to continue seamlessly.