Category BCI

VR and EEG: Real-Time Neurofeedback in Unity

This article demonstrates how Mentalab Explore EEG devices can be integrated with VR headsets to create real-time, brain-driven virtual environments. By processing EEG data in Python, extracting Alpha band power, and streaming it to Unity via Lab Streaming Layer, mental states directly influence immersive VR feedback. The prototype shows how EEG-based biofeedback in virtual reality can move beyond flat screens and support more natural, interactive research and training applications.

A set of emotions drawn on some colourful yellow balls

Detecting Emotion [Using EEG & Fuzzy Logic]

Students at the University of Michigan's Myelin group have made significant strides in detecting human emotion from neurological signals using EEG and machine learning. By leveraging a custom-built video game and Mentalab's mobile EEG headset, they achieved an impressive 94.7% accuracy in anger detection through the innovative application of fuzzy logic for data classification.

Music from Mentalab

Creating Music With Mentalab Explore [EEGSynth]

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.

p300-face

P300 Event Related Potentials

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.

sparkling-brain

EEG SSVEP Classifier [The Basics]

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.