An Analysis of the Correlation between the Asymmetry of Different EEG-Sensor Locations in Diverse Frequency Bands and Short-Term Subjective Well-Being Changes.

We focus on finding a correlation between the asymmetries of electroencephalography (EEG) signals and subjective well-being (SWB) when changed on short time scales via environmental conditions. Most research in this field focuses on frontal alpha asymmetry. We systematically examine different sensor locations and filter the sensor data into the delta band, the theta band, the alpha band, the beta band, and the gamma band, or leave the EEG signal unfiltered. We confirm that frontal alpha asymmetry is correlated to SWB. However, asymmetries between other sensors and/or filtering the data to other bands also shows a linear correlation to SWB values. Asymmetries of anterior brain regions show statistically significant results not only in the alpha band but also in the delta band and theta band, or when the data is not filtered into a specific band. Asymmetries of posterior regions show a trend to be correlated to SWB when EEG activity is higher on the opposite hemisphere and filtered into different frequency bands. Thus, our results let us conclude that focusing just on frontal sensors and the alpha band might not reveal the whole picture of brain regions and frequency bands involved in SWB.

Analysis of the Correlation between Frontal Alpha Asymmetry of Electroencephalography and Short-Term Subjective Well-Being Changes.

Subjective well-being (SWB) describes how well people experience and evaluate their current condition. Previous studies with electroencephalography (EEG) have shown that SWB can be related to frontal alpha asymmetry (FAA). While those studies only considered a single SWB score for each experimental session, our goal is to investigate such a correlation for individuals with a possibly different SWB every 60 or 30 s. Therefore, we conducted two experiments with 30 participants each. We used different temperature and humidity settings and asked the participants to periodically rate their SWB. We computed the FAA from EEG over different time intervals and associated the given SWB, leading to pairs of (FAA, SWB) values. After correcting the imbalance in the data with the Synthetic Minority Over-sampling Technique (SMOTE), we performed a linear regression and found a positive linear correlation between FAA and SWB. We also studied the best time interval sizes for determining FAA around each SWB score. We found that using an interval of 10 s before recording the SWB score yields the best results.

Narrative Review: Quantitative EEG in Disorders of Consciousness.

In this narrative review, we focus on the role of quantitative EEG technology in the diagnosis and prognosis of patients with unresponsive wakefulness syndrome and minimally conscious state. This paper is divided into two main parts, i.e., diagnosis and prognosis, each consisting of three subsections, namely, (i) resting-state EEG, including spectral power, functional connectivity, dynamic functional connectivity, graph theory, microstates and nonlinear measurements, (ii) sleep patterns, including rapid eye movement (REM) sleep, slow-wave sleep and sleep spindles and (iii) evoked potentials, including the P300, mismatch negativity, the N100, the N400 late positive component and others. Finally, we summarize our findings and conclude that QEEG is a useful tool when it comes to defining the diagnosis and prognosis of DOC patients.

Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness.

The diagnosis and prognosis of patients with severe chronic disorders of consciousness are still challenging issues and a high rate of misdiagnosis is evident. Hence, new tools are needed for an accurate diagnosis, which will also have an impact on the prognosis. In recent years, functional Magnetic Resonance Imaging (fMRI) has been gaining more and more importance when diagnosing this patient group. Especially resting state scans, i.e., an examination when the patient does not perform any task in particular, seems to be promising for these patient groups. After preprocessing the resting state fMRI data with a standard pipeline, we extracted the correlation matrices of 132 regions of interest. The aim was to find the regions of interest which contributed most to the distinction between the different patient groups and healthy controls. We performed feature selection using a genetic algorithm and a support vector machine. Moreover, we show by using only those regions of interest for classification that are most often selected by our algorithm, we get a much better performance of the classifier.

Functional magnetic resonance imaging under anaesthesia of a patient with severe chronic disorders of consciousness.

CLINICAL CASE: We report on a 19-year old male patient who is recovering from near-drowning. The patient was admitted for re-evaluation in a Minimally Conscious State. METHOD: A regular functional Magnetic Resonance Imaging was not possible due to complex motor tics of the patient with sudden flexion and extension movements of arms and legs as well as opisthotonic retroflexion of the head and trunk. Thus, the patient was anaesthetised and functional Magnetic Resonance Imaging was performed under general anaesthesia which was introduced and maintained with Sevoflorane and Fentanyl provided analgesia. Four functional runs were performed and the patient's responses were recorded. During each one of these runs one extremity (dorsum manus or pedis) was stimulated with a brush with an operator-paced frequency of about 2 Hz. RESULTS AND CONCLUSION: Clear responses were found in the somatosensory cortex contra lateral within the post central gyrus during stimulation of the left hand. Considering the other three extremities no significant responses were found. Nevertheless, we conclude that a functional Magnetic Resonance Imaging under anaesthesia is possible for patients with severe chronic disorders of consciousness and brain areas responding to stimuli can be detected.