Dataset Information

Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography.

ABSTRACT: This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color?word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4?8 Hz), alpha (8?13 Hz), and beta (13?30 Hz) bands immediately following the initiation of Stroop stimuli; the mean of the Teager energy in each of these three bands; and the wideband EEG signal line-length and number of peaks. These computational features were extracted from the EEG signals on thirteen electrodes during each stimulus presentation and used as inputs to logistic regression, quadratic discriminant analysis, and k-nearest neighbor classifiers. Two complementary analysis methodologies indicated classification accuracies over subjects of around 80% on a balanced dataset for the logistic regression classifier when information from all electrodes was taken into account simultaneously. Additionally, we found evidence that stress responses were preferentially time-locked to stimulus presentation, and that certain electrode?feature combinations worked broadly well across subjects to distinguish stress states.

SUBMITTER: Blanco JA

PROVIDER: S-EPMC6387350 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography.

Blanco Justin A JA Vanleer Ann C AC Calibo Taylor K TK Firebaugh Samara L SL

Sensors (Basel, Switzerland) 20190125 3

This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color⁻word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4⁻8 Hz), alpha (8⁻13 Hz), and beta (13⁻30 Hz) bands immediately followin ...[more]

PMID: 30691041

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Project description:BackgroundSleep disruption in critically ill adults can result in acute decrements in cognitive function, including delirium, but it is underdiagnosed in the setting of the intensive care unit (ICU). Although sleep stages can be assessed by polysomnography (PSG), acquisition and interpretation of PSG is costly, is labor intensive, is difficult to do over an extended period of time with critically ill patients (multiple days of continuous recording), and may interfere with patient care. In this pilot study, we investigated the feasibility and utility of monitoring sleep in the ICU setting using a portable electroencephalography (EEG) monitor, the SedLine brain monitor.MethodsWe first performed a baseline comparison study of the SedLine brain monitor by comparing its recordings to PSG recorded in a sleep laboratory (n = 3). In a separate patient cohort, we enrolled patients in the ICU who were monitored continuously with the SedLine monitor for sleep disruption (n = 23). In all enrolled patients, we continuously monitored their EEG. The raw EEG was retrieved and sleep stages and arousals were analyzed by a board-certified technologist. Delirium was measured by a trained research nurse using the Confusion Assessment Method developed for the ICU.ResultsFor all enrolled patients, we continuously monitored their EEGs and were able to retrieve the raw EEGs for analysis of sleep stages. Overall, the SedLine brain monitor was able to differentiate sleep stages, as well as capture arousals and transitions between sleep stages compared with the PSG performed in the sleep laboratory. The percentage agreement was 67% for the wake stage, 77% for the non-rapid eye movement (REM) stage (N1 = 29%, N2 = 88%, and N3 = 6%), and 89% for the REM stage. The overall agreement was measured with the use of weighted kappa, which was 0.61, 95% confidence interval, 0.58 to 0.64. In the ICU study, the mean recording time for the 23 enrolled patients was 19.10 hours. There were several signs indicative of poor-quality sleep, where sleep was distributed throughout the day, with reduced time spent in REM (1.38% ± 2.74% of total sleep time), and stage N3 (2.17% ± 5.53% of total sleep time) coupled with a high arousal index (34.63 ± 19.04 arousals per hour). The occurrence of ICU delirium was not significantly different between patients with and without sleep disruption.ConclusionsOur results suggest the utility of a portable EEG monitor to measure different sleep stages, transitions, and arousals; however, the accuracy in measuring different sleep stages by the SedLine monitor varies compared with PSG. Our results also support previous findings that sleep is fragmented in critically ill patients. Further research is necessary to develop portable EEG monitors that have higher agreement with PSG.

Project description:BackgroundBrain computer interfaces (BCI) based on electro-encephalography (EEG) have been shown to detect mental states accurately and non-invasively, but the equipment required so far is cumbersome and the resulting signal is difficult to analyze. BCI requires accurate classification of small amplitude brain signal components in single trials from recordings which can be compromised by currents induced by muscle activity.Methodology/principal findingsA novel EEG cap based on dry electrodes was developed which does not need time-consuming gel application and uses far fewer electrodes than on a standard EEG cap set-up. After optimizing the placement of the 6 dry electrodes through off-line analysis of standard cap experiments, dry cap performance was tested in the context of a well established BCI cursor control paradigm in 5 healthy subjects using analysis methods which do not necessitate user training. The resulting information transfer rate was on average about 30% slower than the standard cap. The potential contribution of involuntary muscle activity artifact to the BCI control signal was found to be inconsequential, while the detected signal was consistent with brain activity originating near the motor cortex.Conclusions/significanceOur study shows that a surprisingly simple and convenient method of brain activity imaging is possible, and that simple and robust analysis techniques exist which discriminate among mental states in single trials. Within 15 minutes the dry BCI device is set-up, calibrated and ready to use. Peak performance matched reported EEG BCI state of the art in one subject. The results promise a practical non-invasive BCI solution for severely paralyzed patients, without the bottleneck of setup effort and limited recording duration that hampers current EEG recording technique. The presented recording method itself, BCI not considered, could significantly widen the use of EEG for emerging applications requiring long-term brain activity and mental state monitoring.

Dataset Information

Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography.

Publications

Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography.

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