EEG based Cognitive Assessment
For a wide range of applications, it is cognitive information that is actually sought in a non-invasive and real-time fashion. Being a measure of brain activity, EEG spectral changes have been used for accurate estimation
of alertness and cognitive workload, (Makeig and Jung, 1995; Pope et al., 1995) and cognitive fatigue. (Trejo, 2004) Furthermore, a number of studies have reported that theta is related to increases in attention, workload, memory load,
and working memory performance, and that a large increase in alpha EEG precedes dozing off during a simple visual task. (Torsvall and Akerstedt, 1988) EEG has been used to monitor the progress of trainees through skill levels or identify
ndices of skill acquisition. One group reported an increase in event-related alpha power that correlated with amount of practice at a shooting task and suggested that it reflected a decrease in cortical activity associated with reduced effort required with expertise. (
Kerick et al., 2004) Another group observed lower coherence associated with less cortico-cortical communication in expert marksmen compared to skilled shooters, and attributed this difference to decreased involvement of cognition with expertise. (Deeny et al., 2003)
Over the past couple of years, QUASAR has developed QStates, a software package that uses quantitative EEG and heart rate variability data for assessment of cognitive and physiological state. This learning algorithm first calculates several thousand spectral EEG features,
then a Partial Least Squares algorithm uses the most salient of these features as inputs for setting weights of cognitive models based on the data collected during calibration runs of defined tasks (e.g. Easy vs. Hard Tasks or Happy vs Sad Tasks). Training models requires as little
as one minute of EEG data for each state (easy vs. hard), and is computationally expedient. EEG data collected during experiments are then classified with these trained models to produce real-time cognitive state measures whose output ranges from 1 to 100 representing the probability of being in one state or the other.
Many efforts at developing cognitive gauges have attempted to produce universal gauges that work for all individuals in order to produce "ready to go" systems. QUASAR's rapidly training algorithms allow for expedient calibration within minutes. These models typically produce average classifications accuracies >90%.
Furthermore, the models' outputs track task difficulty reliably, correctly interpolating cognitive workload for tasks of intermediate difficulty compared to those used for training.
Wearable Sensing is proud to offer QUASAR's QStates among its product line. QStates interfaces seamlessly with DSI-Streamer and the various DSI systems
QStates' Cognitive Workload Performance.
Average classification accuracy of models for mental workload, engagement and fatigue across 18 subjects
Average mental workload model output on 18 subjects across varying task difficulty (avg ± std).