Cognitive State Classification Using Machine Learning
QStates is a rapid and efficient machine learning software tool developed by QUASAR that uses quantitative EEG (qEEG) and heart rate variability (HRV) data to assess cognitive and physiological states. Cognitive state assessment can be done in real-time and off-line with graphical displays of results.
The Flexiblity to Create Your Own Cognitive Models
QStates offers its users the flexibility to create their own models, which can be trained to classify any cognitive state for which there is a qEEG signature (e.g. cognitive workload, fatigue, engagement, or emotional states). Wearable Sensing and QUASAR scientists have validated the performance of greater than 90% accuracy for the states mental workload, engagement, and fatigue.
This flexibility makes QStates a powerful tool for many different types of research, especially neuroergonomics and consumer neuroscience, but also all other cognitive neuroscience applications.
Features and Benefits
Training models are straightforward and fast: to create a cognitive state model, a user needs to collect a minimum of one minute of EEG data for each of the high and low state conditions (e.g. high workload vs. low workload).
QStates can classify up to 3 models simultaneously. The software automatically monitors data quality and rejects bad epochs. The program offers two different machine learning algorithm outputs: linear interpolation or probability density function. State outputs are given as values from 0-100 every 2 seconds. QStates offers automated summary tables generation. Data are saved in comma-separated value (.csv) format. QStates interfaces seamlessly with DSI-Streamer and the various DSI EEG systems.