Classification of mental workload using brain connectivity and machine learning on electroencephalogram data.

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was determined by analyzing their pretreatment EEG signals, which were then concatenated into delta, theta, alpha, and beta bands and transformed into images. Using these images, we then fine tuned a hybrid Convolutional Neural Network that is enhanced with bidirectional Long Short-Term Memory cells based on transfer learning. The Inception-v3, ResNet18, DenseNet121, and EfficientNet-B0 models are implemented as base models. Finally, the models are followed by BiLSTM and dense layers in order to classify responders and non-responders to SSRI treatment. Results showed that the EfficiencyNet-B0 has the highest accuracy of 98.33, followed by DensNet121, ResNet18 and Inception-v3. Therefore, a new method was proposed in this study that uses deep learning models to extract both spatial and temporal features automatically, which will improve classification results. The proposed method provides accurate identification of MDD patients who are responding, thereby reducing the cost of medical facilities and patient care.

Sleep Micro-Macro-structures in Psychophysiological Insomnia. PSG Study.

Study Objectives: To address sleep micro-macro-structures in psychophysiological insomnia (PPI) as denoted by cyclic alternating pattern (CAP), Sleep spindles, and hyperarousal as microstructures and sleep characteristics such as sleep stages' variables, and heart rate as macrostructures. Methods: Two statistical populations, with 20 participants in each, are addressed: good sleepers (GS) and patients with psychophysiological insomnia (PPI). The sleep polysomnography (PSG) for one night was performed and sleep macro-micro-structures extraction was implemented for each participant. Cyclic alternating patterns were scored manually and other structures were monitored by the original PSG's device software. Analytical methods are used to dissect the results. Result: The findings imply: (a) psychophysiological insomnia is characterized by CAP differences from good sleepers which are associated with hyperarousal; (b) Regarding microstructure, more microarousals in sleep stages caused more number of wake index. (c) The ratio of sleep stages, sleep latency and heart rate as sleep macrostructure are significantly changed. (d) There is no significant difference between PPI and GS groups on spindles length in our research. Conclusion: Regarding all sleep disorders and especially PPI, CAP variables, EEG arousals, and sleep spindles as microstructures and Total Sleep Time, Sleep Latency, number of waking, REM duration, and Heart Rate as macrostructures were found to be critical for the diagnosis of psychophysiological insomnia The analysis contributes to understanding better approaches in the quantitative specification of psychophysiological insomnia compare to good sleepers.

An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals.

One of the most effective treatments for drug-resistant Major depressive disorder (MDD) patients is repetitive transcranial magnetic stimulation (rTMS). To improve treatment efficacy and reduce health care costs, it is necessary to predict the treatment response. In this study, we intend to predict the rTMS treatment response in MDD patients from electroencephalogram (EEG) signals before starting the treatment using machine learning approaches. Effective brain connectivity of 19-channel EEG data of MDD patients was calculated by the direct directed transfer function (dDTF) method. Then, using three feature selection methods, the best features were selected and patients were classified as responders or non-responders to rTMS treatment by using the support vector machine (SVM). Results on the 34 MDD patients indicated that the Fp2 region in the delta and theta frequency bands has a significant difference between the two groups and can be used as a significant brain biomarker to assess the rTMS treatment response. Also, the highest accuracy (89.6%) using the SVM classifier for the best features of the dDTF method based on the area under the receiver operating characteristic curve (AUC-ROC) criteria was obtained by combining the delta and theta frequency bands. Consequently, the proposed method can accurately detect the rTMS treatment response in MDD patients before starting treatment on the EEG signal to avoid financial and time costs to patients and medical centers.

Combined Yoga and Transcranial Direct Current Stimulation Increase Functional Connectivity and Synchronization in the Frontal Areas.

Transcranial direct current stimulation (tDCS) is a non-invasive neuro-stimulation technique that can modulate cortical excitability. Similarly, yoga is shown to affect the brain's neural activity and networks. Here, we aimed to investigate the effect of combined yoga and tDCS on brain oscillations and networks using resting-state electroencephalography recordings. In a randomized, cross-over, double-blind design, twenty-two healthy subjects participated in a yoga/active tDCS session (2 mA; 20 min; anode-F3, cathode F4) or yoga/sham tDCS on 2 separate days. Resting-state EEG data were collected before and after each intervention. Power spectral density (PSD) and functional connectivity, measured by a synchronization measure, phase-locking value, were computed for each condition. There were no significant differences in PSD values among the two interventions. The network-based statistic method was employed for detecting functional connectivity differences between yoga/active and yoga/sham tDCS interventions. Results show that the addition of active tDCS to yoga is associated with increased functional connectivity of the scalp and source EEG data in the frontal area. The changes were widespread, intra-hemispheric, and inter-hemispheric connections, which were mainly between the frontal area to other regions. At the source level, most of the connectivity changes were found in the fronto-parietal network. These findings suggest that combining yoga with tDCS might lead to brain network changes related to the executive and attentional functions.

Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal.

INTRODUCTION: Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process. METHODS: In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes. RESULTS: Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia. CONCLUSION: TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.

Monitoring the level of hypnosis using a hierarchical SVM system.

Monitoring level of hypnosis is a major ongoing challenge for anesthetists to reduce anesthetic drug consumption, avoiding intraoperative awareness and prolonged recovery. This paper proposes a novel automated method for accurate assessing of the level of hypnosis with sevoflurane in 17 patients using the electroencephalogram signal. In this method, a set of distinctive features and a hierarchical classification structure based on support vector machine (SVM) methods, is proposed to discriminate the four levels of anesthesia (awake, light, general and deep states). The first stage of the hierarchical SVM structure identifies the awake state by extracting Shannon Permutation Entropy, Detrended Fluctuation Analysis and frequency features. Then deep state is identified by extracting the sample entropy feature; and finally light and general states are identified by extracting the three mentioned features of the first step. The accuracy of the proposed method of analyzing the brain activity during anesthesia is 94.11%; which was better than previous studies and also a commercial monitoring system (Response Entropy Index).

Frontal-temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia.

Quantifying brain dynamics during anesthesia is an important challenge for understanding the neurophysiological mechanisms of anesthetic drug effect. Several single channel Electroencephalogram (EEG) indices have been proposed for monitoring anesthetic drug effect. The most commonly used single channel commercial index is the Bispectral index (BIS). However, this monitor has shown some drawbacks. In this study, a nonlinear functional connectivity measure named Standardized Permutation Mutual Information (SPMI) is proposed to describe communication between two-channel EEG signals at frontal and temporal brain regions during a controlled propofol-induced anesthesia and recovery design from eight subjects. The SPMI index has higher correlation with estimated propofol effect-site concentration and has better ability to distinguish three anesthetic states of patient than the other functional connectivity indexes (cross-correlation, coherence, phase analysis) and also the BIS index. Moreover, the SPMI index has a faster reaction to the effect of drug concentration, less variability at the consciousness state and better robustness to noise than BIS.

Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression.

Background: Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a non-invasive neurophysiological test that has promise as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel non-linear index of resting state EEG activity as a predictor of clinical outcome, and compare its predictive capacity to traditional frequency-based indices. Methods: EEG was recorded from 62 patients with treatment resistant depression (TRD) and 25 healthy comparison (HC) subjects. TRD patients were treated with excitatory repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) for 4 to 6 weeks. EEG signals were first decomposed using the empirical mode decomposition (EMD) method into band-limited intrinsic mode functions (IMFs). Subsequently, Permutation Entropy (PE) was computed from the obtained second IMF to yield an index named PEIMF2. Receiver Operator Characteristic (ROC) curve analysis and ANOVA test were used to evaluate the efficiency of this index (PEIMF2) and were compared to frequency-band based methods. Results: Responders (RP) to rTMS exhibited an increase in the PEIMF2 index compared to non-responders (NR) at F3, FCz and FC3 sites (p < 0.01). The area under the curve (AUC) for ROC analysis was 0.8 for PEIMF2 index for the FC3 electrode. The PEIMF2 index was superior to ordinary frequency band measures. Conclusion: Our data show that the PEIMF2 index, yields superior outcome prediction performance compared to traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression; specifically entropy indices applied in band-limited EEG components. Registration in ClinicalTrials.Gov; identifiers NCT02800226 and NCT01887782.

Monitoring the Depth of Anesthesia Using a New Adaptive Neurofuzzy System.

Accurate and noninvasive monitoring of the depth of anesthesia (DoA) is highly desirable. Since the anesthetic drugs act mainly on the central nervous system, the analysis of brain activity using electroencephalogram (EEG) is very useful. This paper proposes a novel automated method for assessing the DoA using EEG. First, 11 features including spectral, fractal, and entropy are extracted from EEG signal and then, by applying an algorithm according to exhaustive search of all subsets of features, a combination of the best features (Beta-index, sample entropy, shannon permutation entropy, and detrended fluctuation analysis) is selected. Accordingly, we feed these extracted features to a new neurofuzzy classification algorithm, adaptive neurofuzzy inference system with linguistic hedges (ANFIS-LH). This structure can successfully model systems with nonlinear relationships between input and output, and also classify overlapped classes accurately. ANFIS-LH, which is based on modified classical fuzzy rules, reduces the effects of the insignificant features in input space, which causes overlapping and modifies the output layer structure. The presented method classifies EEG data into awake, light, general, and deep states during anesthesia with sevoflurane in 17 patients. Its accuracy is 92% compared to a commercial monitoring system (response entropy index) successfully. Moreover, this method reaches the classification accuracy of 93% to categorize EEG signal to awake and general anesthesia states by another database of propofol and volatile anesthesia in 50 patients. To sum up, this method is potentially applicable to a new real-time monitoring system to help the anesthesiologist with continuous assessment of DoA quickly and accurately.

Automatic computation of left ventricular volume changes over a cardiac cycle from echocardiography images by nonlinear dimensionality reduction.

Curve of left ventricular (LV) volume changes throughout the cardiac cycle is a fundamental parameter for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is often performed manually which is tedious and time consuming and suffers from significant interobserver and intraobserver variability. This paper introduces a new automatic method, based on nonlinear dimensionality reduction (NLDR) for extracting the curve of the LV volume changes over a cardiac cycle from two-dimensional (2-D) echocardiography images. Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this study, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on 2-D echocardiography images of one cycle of heart. Using this approach, the nonlinear information of these images is embedded in a 2-D manifold and each image is characterized by a symbol on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes and allows extracting the curve of the LV volume changes automatically. Our method in comparison to the traditional segmentation algorithms does not need any LV myocardial segmentation and tracking, particularly difficult in the echocardiography images. Moreover, a large data set under various diseases for training is not required. The results obtained by our method are quantitatively evaluated to those obtained manually by the highly experienced echocardiographer on ten healthy volunteers and six patients which depict the usefulness of the presented method.

Frontal-temporal synchronization of EEG signals quantified by order patterns cross recurrence analysis during propofol anesthesia.

Characterizing brain dynamics during anesthesia is a main current challenge in anesthesia study. Several single channel electroencephalogram (EEG)-based commercial monitors like the Bispectral index (BIS) have suggested to examine EEG signal. But, the BIS index has obtained numerous critiques. In this study, we evaluate the concentration-dependent effect of the propofol on long-range frontal-temporal synchronization of EEG signals collected from eight subjects during a controlled induction and recovery design. We used order patterns cross recurrence plot and provide an index named order pattern laminarity (OPL) to assess changes in neuronal synchronization as the mechanism forming the foundation of conscious perception. The prediction probability of 0.9 and 0.84 for OPL and BIS specified that the OPL index correlated more strongly with effect-site propofol concentration. Also, our new index makes faster reaction to transients in EEG recordings based on pharmacokinetic and pharmacodynamic model parameters and demonstrates less variability at the point of loss of consciousness (standard deviation of 0.04 for OPL compared with 0.09 for BIS index). The result show that the OPL index can estimate anesthetic state of patient more efficiently than the BIS index in lightly sedated state with more tolerant of artifacts.

Monitoring the depth of anesthesia using entropy features and an artificial neural network.

Monitoring the depth of anesthesia using an electroencephalogram (EEG) is a major ongoing challenge for anesthetists. The EEG is a recording of brain electrical activity, and it contains valuable information related to the different physiological states of the brain. This study proposes a novel automated method consisting of two steps for assessing anesthesia depth. Initially, the sample entropy and permutation entropy features were extracted from the EEG signal. Because EEG-derived parameters represent different aspects of the EEG features, it would be reasonable to use multiple parameters to assess the effect of the anesthetic. The sample entropy and permutation entropy features quantified the amount of complexity or irregularity in the EEG data and were conceptually simple, computationally efficient and artifact-resistant. Next, the extracted features were used as input for an artificial neural network, which was a data processing system based on the structure of a biological nervous system. The experimental results indicated that an overall accuracy of 88% could be obtained during sevoflurane anesthesia in 17 patients to classify the EEG data into awake, light, general and deep anesthetized states. In addition, this method yielded a classification accuracy of 92.4% to distinguish between awake and general anesthesia in an independent database of propofol and desflurane anesthesia in 129 patients. Considering the high accuracy of this method, a new EEG monitoring system could be developed to assist the anesthesiologist in estimating the depth of anesthesia in a rapid and accurate manner.