Electroencephalogram (EEG) individual differences are a critical factor influencing EEG-based emotion recognition, yet they have not been thoroughly investigated, hindering the development of affective Brain-Computer Interfaces (aBCI). Facing the lack of EEG information decoding research, we conducted an interpretable study on EEG individual differences using five datasets (SEED, SEED-IV, SEED-V, RCLS, and MPED). We analyzed the impact of different EEG information (individual, session, emotion, and trial) through sample space visualization, aggregation phenomena quantification, and energy pattern analysis. By examining emotional difference feature distribution patterns, we identified the Cross-Session Consistency of Individual Emotional Patterns (CCIEP) and the Individual Emotional Pattern Difference (IEPD). These characteristics are the main factors impacting emotion recognition stability. To quantify emotional patterns, we proposed the Correction T-test (CT) weight extraction method. Leveraging individual emotional pattern and trial information, we developed the Weight-based Channel-model Matrix Framework (WCMF) to address limitations of traditional modeling approaches caused by IEPD. Finally, WCMF was validated on cross-dataset tasks through two practical scenario experiments. The results demonstrated that WCMF achieves more stable and superior performance compared to traditional methods. This study provides a deeper understanding of EEG individual differences and offers a robust framework to advance aBCI systems.

Electroencephalogram (EEG) has been widely used for emotion recognition due to its portability and high temporal resolution. It makes success in subject-dependent scenario but faces significant challenges in cross-subject emotion recognition because of non-stationarity of EEG and individual differences. Most previous studies treat all individuals as a single source domain for transferring emotional knowledge, which may introduce irrelevant information and lead to negative transfer. Besides, there is a potential risk that some important information of common emotional features might be ignored. To deal with the issues, we propose a framework called hybrid source selection fusion domain-invariant attention (HSSFDA) for cross-subject emotion recognition. First, source domains are selected by leveraging local and global similarity for knowledge transfer. Then, a specialized attention mechanism is employed to focus on important emotional information extracted from the domain-invariant features. Finally, domain-invariant and domain-specific features are fused to enhance emotion recognition performance. To evaluate the proposed method, experiments are conducted on several public datasets including SEED, SEED_IV, DREAMER and DEAP. The results demonstrate that HSSFDA achieves accuracies of 85.07 %, 72.11 %, 62.36 %, 77.17 %, 58.51 %, and 63.55 % on SEED, SEED_IV, valence and arousal of DREAMER, and valence and arousal of DEAP datasets, respectively, demonstrating competitive performance compared to popular and state-of-the-art methods. Furthermore, we apply the HSSFDA to a self-recorded dataset collected by self-developed three-channel device and validate its effectiveness in practical applications. In conclusion, HSSFDA is a feasible method for cross-subject emotion recognition and has the potential to broaden the application of EEG in the field of affective computing.

Cross-subject emotion recognition remains a challenge due to inter-subject variability, which limits the generalized ability of models to unseen subjects. Existing studies commonly rely on tasking-state EEG data from the target subject for adaptation, which requires additional emotion-elicitation experiments and limits practical deployment. Motivated by findings that resting-state EEG can reflect individual-specific neural characteristics, this study proposes a Discriminative Knowledge Fuzzy Transfer Learning Guided by Resting-state EEG (DKFTL-R) for cross-subject emotion recognition without requiring tasking-state EEG data from the target subject. First, resting-state EEG is leveraged to characterize subject-specific neural signatures, by which source-domain selection is informed. Second, an emotion-style projection alignment module is introduced, in which discriminative emotional knowledge and domain-specific style are integrated via adaptive weighting so that a more transferable representation is obtained. Finally, a Takagi-Sugeno-Kang fuzzy classifier is employed to perform fuzzy inference on the transferable representation. Experiments are conducted on DEAP and DENS datasets, where accuracies of 58.79%, 55.89%, 62.91%, and 60.42% are achieved, respectively, demonstrating competitive performance compared with popular and recent baseline methods. To evaluate practical applicability and deployability, the proposed method is conducted on a self-constructed emotion EEG dataset (BHE-EMO), and it achieves 67.00% accuracy for two-class classification and 44.92% for three-class classification tasks, further demonstrating its effectiveness and engineering potential in real-world settings. In conclusion, we propose a new perspective on cross-subject emotion recognition by integrating resting-state EEG information with fuzzy modeling. This study also introduces a new calibration paradigm for affective brain-computer interface systems.

Feature selection is a crucial step in EEG emotion recognition. However, it was often used as a single objective problem to either reduce the number of features or maximize classification accuracy, while neglecting their balance. To address the issue, we proposed Improved Multi-objective Grey Wolf Optimization Feature Selection (IMGWOFS). First, we designed a population initialization operator via discriminability and independence of features to accelerate search speed. Second, we employed a two-stage update strategy to improve the global search capabilities of the EEG feature subsets. Finally, we incorporated an adaptive mutation operator to escape the local optima. We conducted experiments on SEED and DEAP datasets, and the accuracy were 86.87$\pm$1.62 % and 60.65$\pm$1.51 % in the beta band using a smaller number of EEG features. In addition, the frontal lobe was related to emotion processing. In conclusion, IMGWOFS is an effective and feasible feature selection method for EEG-based emotion recognition.

Currently, treatment options for patients with treatment-resistant depression (TRD) are extremely limited. Although the rapid antidepressant effects of nitrous oxide have been preliminarily validated, the underlying neurophysiological mechanisms remain unclear. In this study, we intended to explore impact on brain modules induced by nitrous oxide through a randomized controlled study. A total of 44 patients with TRD were recruited. Subjects were randomly allocated to nitrous oxide-intervention group (1-hour inhalation of 50% nitrous oxide/50% oxygen) or placebo-control group (1-hour inhalation of 50% oxygen/50% air). The eye-closed resting state EEG signals were recorded. Our findings reveal that nitrous oxide treatment significantly altered the segregation of interconnected brain functional modules. The result of General Linear Model (repeated measurements) demonstrates superior antidepressant efficacy of nitrous oxide compared to placebo, as evidenced by reductions in both the participation coefficient and connector hub after treatment. Furthermore, changes in modular metrics moderately correlated with reductions in depressive symptoms. These findings offer valuable insights into mechanism underlying the treatment of TRD using nitrous oxide from a modular perspective.

Depression is a complex mental disease with its pathological mechanism unclear. To depict the complete picture of the abnormal information interaction in a depressed brain, this study is the first to apply fully connected multilayer brain functional (FCMBF) network framework and proposed composite FCMBF (CFCMBF) network framework, combined with graph theory to analyze the within-frequency coupling (WFC) and cross-frequency coupling (CFC) of sensor-layer and source-layer electroencephalography (EEG) signals in relevant subjects. Results showed that in the sensor-layer FCMBF network, depressive patients showed significantly reduced functional connectivity, as well as abnormal global and local information processing abilities of the network, and these network properties were significantly correlated with depressive symptoms. In addition, from the perspective of depression recognition, we found that the sensor-layer CFCMBF network could achieve better classification accuracy, especially when using the overlapping degree of node under the right center region, its accuracy could reach$86.88\% \pm 9.25 \%$. More importantly, the construction of the CFCMBF network has higher time efficiency and less information loss, since it not only measures the WFC and CFC between brain region representative signals (BRRSs) extracted from different brain regions, but also measures these two couplings between all nodes within each brain region. Although the FCMBF network contains more complete information by calculating WFC and CFC between all nodes distributed in each region, it will result in an enormous computational cost. In summary, this study proved the utility of multilayer brain network in revealing the abnormal brain interaction patterns of depression, and our proposed method might provide methodological support for efficient depression recognition research based on multilayer brain networks.

Many studies attempt to explore the underlying mechanisms of depression and distinguish between depression patients and normal controls (NC) using electroencephalography (EEG) techniques. With the advancement of deep learning methods, an increasing number of studies aim to design Computer-Aided Diagnosis (CAD) systems for mild depression (MD) to achieve early identification. However, few studies construct models based on abnormal brain topological structures specific to MD patients. In this study, we investigate the abnormal brain topological structures of individuals with MD based on EEG data obtained during an emotional face paradigm. Functional connectivity analysis reveals a higher proportion of inter-hemispheric connections in the MD group compared to intra-hemispheric connections. Additionally, intra-hemispheric connections are primarily observed within the frontal and parietal lobes of both groups. Hierarchical clustering analysis results indicate impairments in the frontal and parietal lobes in the MD group compared to the NC group. Based on these findings, we propose a novel feature called "cross-brain feature" and introduce a multi-cross-brain attention fusion mechanism to integrate information between brain regions. We train and test our models using 5-fold cross-validation. The results demonstrate that the classification model based on abnormal brain topological structures achieves the highest performance among the three state-of-the-art algorithms, with an accuracy of 80.1%, an area under the ROC curve (AUC) of 80%, and a sensitivity (SEN) of 86.3%. These findings suggest that combining abnormal brain topological structures derived from functional connectivity matrices with deep learning techniques can provide an effective objective approach to the early detection of depression.

Deep learning methods have been widely adopted in the field of computer-aided EEG diagnosis, one of the major topics to be investigated is the input format of EEG data. Related researches have reported the application of Fast Fourier Transform (FFT) to topology-preserving multi-spectral images generation on three frequency bands (i.e. theta, alpha and beta) jointed to preserve the spatial information. In our work, we proposed a new approach of using Empirical Mode Decomposition (EMD) instead of FFT algorithm to generate topology-preserving multi-spectral images on intrinsic mode functions (IMFs) jointed and only on the single IMF. Meanwhile, images generated on three frequency bands jointed and the single frequency band were also used for comparison. We then applied two convolutional neural network (CNN) structures to distinguish depression patients and normal subjects from the topology-preserving multi-spectral images. As a result, both convolutional networks obtain better accuracies on three IMFs jointed (about 5% better, ≈ 75% vs. ≈ 70%) than three frequency bands jointed. Analysis based on the single frequency band indicates that alpha band performs the best, with an accuracy of more than 78%, and among all classification results, the best classification accuracy obtained is 80.23% on IMF2. The results are encouraging, despite the limited size of our cohort, the use of EMD and our findings cast a new light on application of deep learning method to EEG-based depression recognition.

It was observed that accuracy of the Subject-Dependent emotion recognition model was much higher than that of the Subject-Independent model in the field of electroencephalogram (EEG) based affective computing. This phenomenon is mainly caused by the individual difference of EEG, which is the key issue to be solved for the application of emotion recognition. In this work, 14 subjects from the SEED were selected for individual difference analysis. Through individual aggregation features evaluation, sample space visualization, and correlation analysis, we proposed four quantification indicators to analyze individual difference phenomenon. Finally, we presented the Personal-Zscore (PZ) feature processing method, and it was found that the data set processed with PZ method could represent emotion better than the original data set, and the conventional model with the PZ method was more robust. The accuracies of emotion recognition models trained with PZ processing have been improved to some extent, which showed that the PZ method could effectively eliminate the individual aggregation of feature space and improve the emotional representation ability of data sets. Hence, our findings may provide a new insight into the foundation for universal implementation of EEG-based application, and the Personal-Zscore feature processing method is of great significance for the development of effective emotion recognition system.

Emotional conflict control is impaired in major depression disorders (MDDs) and affects decision-making with further consequent social interactions dysfunction. However, neural correlates of conflict monitoring processes being modulated by different affective distractor stimuli are not clear in MDDs. In this article, we investigated abnormal neural basis of conflict monitoring processes in MDD patients by applying dynamic causal modeling (DCM) technique on electroencephalography (EEG). The results indicated that MDD patients showed lower N2 amplitudes regardless of stimulus conditions, and reduced activation within ACC region for incongruent stimuli, relative to healthy controls. Especially, MDDs had more negative N2 amplitudes to happy incongruent trials than happy congruent trials. Source localization analyses revealed that MDD patients had significantly enhanced left inferior temporal gyrus (ITG) activation, which is involved in written words processing. Further DCM analysis provided abnormal neural correlates through greater backward connections (fusiform→ITG, amygdala→ITG) on happy incongruent trials than happy congruent trials in MDD group. These findings indicate that only sad words induce significantly greater interference effects to positive target faces in MDD patients, which may be associated with ITG activity dysfunction. The findings may share new insights into the neural mechanisms of emotional conflict processing in MDDs.

Major depressive disorder (MDD) may be driven by dysfunction in intrinsic dynamic properties of the brain, and EEG microstate is a promising method for analyzing brain dynamics. However, the alterations in EEG microstate is still not entirely clear, and its ability for MDDs detection is worth probing. Moreover, the mechanism behind the neural networks contributing to microstates remains poorly understood in MDDs. Therefore, we applied microstate analysis and Topographic Electrophysiological State Source-imaging (TESS) on EEG data of 27 MDDs and 28 healthy controls (HCs). Compared to HCs, MDDs had apparent increase in microstate C and decrease in microstate D. Furthermore, TESS results showed that the underlying network of microstate C in MDDs overlapped with the anterior cingulate cortex and left insula gyrus, whereas main source of microstate D was in the orbital part of inferior frontal gyrus. The reduced transition probability from C to D in MDDs may reveal an imbalance between the networks of microstates. The microstate parameters as features reached good performance in identifying MDD (89.09% accuracy, 92.86% sensitivity, 85.19% specificity), indicating their potential as biomarkers of depression pathology. Collectively, these results highlight alteration of brain activity patterns and provide new insights into abnormal EEG dynamics in MDDs.

The detection of mild depression is conducive to the early intervention and treatment of depression. This study explored the fusion of electroencephalography (EEG) and pupil area signals to build an effective and convenient mild depression recognition model. We proposed Mutual Information Based Fusion Model (MIBFM), which innovatively used pupil area signals to select EEG electrodes based on mutual information. Then we extracted features from EEG and pupil area signals in different bands, and fused bimodal features using the denoising autoencoder. Experimental results showed that MIBFM could obtain the highest accuracy of 87.03%. And MIBFM exhibited better performance than other existing methods. Our findings validate the effectiveness of the use of pupil area as signals, which makes eye movement signals can be easily obtained using high resolution camera, and the EEG electrode selection scheme based on mutual information is also proved to be an applicable solution for data dimension reduction and multimodal complementary information screening. This study casts a new light for mild depression recognition using multimodal data of EEG and pupil area signals, and provides a theoretical basis for the development of portable and universal application systems.

OBJECTIVE: Depression is accompanied by abnormalities in large-scale functional brain networks. This paper combined static and dynamic methods to analyze the abnormal topology and changes of functional connectivity network (FCN) of depression. METHODS: We collected resting-state EEG recordings from 27 depressed subjects and 28 normal subjects, then obtained 68 regions of interests (ROIs) by source localization. We took ROIs as the nodes and correlations as the edges to build FCNs and analyzed static network based on graph theory. We used a sliding window method followed by k-means clustering, states analyses and trend analysis of network metrics over time to study dynamic connectivity. RESULTS: The clustering coefficient (CC) and local efficiency in depression were increased, the characteristic path length and global efficiency were decreased, and local metrics had different manifestations in different resting state networks (RSNs); Depression had reduced connectivity in most RSNs, but increased connectivity in the default mode network, and there was a decoupling phenomenon between different RSNs; Depressed patients spent more time in sparsely connected states, their FCN's flexibility was less than normal subjects; The trend of CC over time was opposite between two groups. Most metrics in normal showed a relatively stronger correlation with time. SIGNIFICANCE: Our research may provide a deeper understanding of neurophysiological mechanisms of depression and new biomarkers for clinical diagnosis of depression.

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

In recent years, depression recognition using physiological signals has achieved certain progress, but mild depression recognition is still in its infancy. Early detection can prevent the development of depression, and combining multiple modalities for analysis has been proved effective in the domain of mental disorders detection. Electroencephalogram (EEG) and eye movements (EM) are widely used to identify depression. However, the problem of using physiological signals to detect mental illness is that the generalization ability of the model is not strong, which is caused by individual differences. In view of the above problems, this paper proposes a hybrid fusion model based on deep belief network (DBN) and secondary classifier, called HFMBDSC, which first uses unsupervised DBN to fuse EEG features (linear, nonlinear features and network features) at the feature level. DBN transforms EEG features into another form to mitigate the effects of individual differences in EEG, and obtains DBN features that can more comprehensively represent EEG information. In the next step of decision level fusion, DBN features and EM features jointly make the final decision using the three classifiers that perform best when using single modality. The results show that DBN can improve the classification accuracy and effectively reduce the influence of individual differences in EEG features through visual analysis of feature space. The classification performance of HFMBDSC is significantly improved compared to the traditional single modality results. The highest accuracy rate of 89.54% is obtained under 10-fold cross-validation. These results suggest that mild depression recognition based on HFMBDSC is promising.

Electroencephalography (EEG) have been proved to be effective in the field of depression recognition, however, the application of EEG-based mild depression detection is still in its infancy. Our work mainly focused on mild depression recognition of college students, based on high-density 128-channel EEG recordings from 24 mild depression individuals and 24 normal subjects using facial expression as experimental materials. In order to prevent individual performance differences on the convolution kernel, and to integrate time information and spatial information instead of simply combining, we proposed a new deep learning model with multiple convolution kernels and a Long Short-Term Memory (LSTM) strategy based on convolution. Batch normalization has been widely used and proved to be effective in some research areas, for example computer vision. Our findings show that for EEG data, batch normalization will reduce the accuracy due to the special data characteristics of EEG. It was found that the proposed model achieved an accuracy of 83.47% with the 8-fold cross-validation, and Batch Normalization will reduce the accuracy because it eliminated the difference between depression and normal. Our findings cast a new light to recognize mild depression accurately and quickly, it could be used as auxiliary tools to diagnose and predict mild depression in the future.

This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Resting-state EEG data were collected from 24 major depressive patients (MDD) and 29 normal controls using 128-electrode geodesic sensor net. To better identify depression, we extracted multi-type of EEG features including linear features (L), nonlinear features (NL), functional connectivity features phase lagging index (PLI) and network measures (NM) to comprehensively characterize the EEG signals in patients with MDD. And machine learning algorithms and statistical analysis were used to evaluate the EEG features. Combined multi-types features (All: L+ NL + PLI + NM) outperformed single-type features for classifying depression. Analyzing the optimal features set we found that compared to other type features, PLI occupied the largest proportion of which functional connections in intra-hemisphere were much more than that of in inter-hemisphere. In addition, when using PLI features and All features, high frequency bands (alpha, beta) could achieve obviously higher classification accuracy than low frequency bands (delta, theta). Parietal-occipital lobe in the high frequency bands had great effect in depression identification. In conclusion, combined multi-types EEG features along with a robust classifier can better distinguish depressive patients from normal controls. And intra-hemispheric functional connections might be an effective biomarker to detect depression. Hence, this paper may provide objective and potential electrophysiological characteristics in depression recognition.

Previous studies have shown that functional changes in depression are a context-specific rather than generalized across stimuli. In our study, with the aim of investigating the differences in the functional networks, electroencephalogram (EEG) data were collected from 27 mild depression (MD) and 27 normal controls (NC) using the Cue - Target paradigm. The exact low resolution electromagnetic tomography (eLORETA) method is applied to estimate the three-dimensional distribution of the current density of the brain source, and lagged coherence(LC), lagged phase synchronization(LPS), lagged linear connectivity(LLC), lagged nonlinear connectivity(LNC) are used to calculate the functional connections between pairs of regions of interest. In the four frequency bands of delta, theta, alpha and beta, the clustering coefficient (CC) and characteristic path length (CPL) were calculated and statistical analysis was performed. Our research found that the electrophysiological activity of MD in Brodmann area (BA) 7 was stronger than that of NC in all frequency bands. When the cue is color block and the prompt is inconsistent with the target, the CC and CPL of MD and NC were significantly different in the delta, theta, and beta frequency band. This result showed that the functional network change of MD was most obvious when cue is an arrow and the cue is inconsistent with the target. In this condition, the CC and CPL of MD are greater than that of NC, indicating that these network characteristics might be used as biological indicators to identify MD.

Major depression disorder (MDD) has been proved to have difficulty in emotional conflict processing. The objective of this study is to investigate the different attention deployment patterns in emotional conflict processing between MDDs and Healthy Controls (HCs) using eye tracking data. A face-word Stroop task was used, and 49 MDDs and 50 healthy controls (HCs) were recruited in the experiment. Finally, our results indicated that MDDs demonstrated lower accuracy (ACC) compared with HCs during the process of emotional conflict. Moreover, we found attention bias in the process of attention maintenance but not vigilance, and it may be one of the possible reasons for different ability of emotional conflict processing between MDDs and HCs.

Depression, influencing millions of people, has become a major disease in the past decade. However, the assessment methods of diagnosing depression almost exclusively rely on patient-reported or clinical judgments of symptom severity, which are associated with subjective biases and intensive labor. Some bio-signals such as EEG and eye movements are used for automatic detection but their accuracies are not accurate enough for the real application, further improvements are needed. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, generating data subsets by the content of the experiment, then using the majority vote of subsets to determine the subjects' label. The validation of the method is testified by two different experiments which included free viewing eye tracking and task-state EEG and these two experiments have 36, 40 subjects respectively. In these two experiments CBEM gains accuracies of 82.5% and 92.73% respectively. The results show that CBEM outperform traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and give an objective and quantitative evaluation of depression, which in the future could be used for the auxiliary diagnosis of depression.

The research detailed in this paper focuses on the processing of Electroencephalography (EEG) data to identify attention during the learning process. The identification of affect using our procedures is integrated into a simulated distance learning system that provides feedback to the user with respect to attention and concentration. The authors propose a classification procedure that combines correlation-based feature selection (CFS) and a k-nearest-neighbor (KNN) data mining algorithm. To evaluate the CFS+KNN algorithm, it was test against CFS+C4.5 algorithm and other classification algorithms. The classification performance was measured 10 times with different 3-fold cross validation data. The data was derived from 10 subjects while they were attempting to learn material in a simulated distance learning environment. A self-assessment model of self-report was used with a single valence to evaluate attention on 3 levels (high, neutral, low). It was found that CFS+KNN had a much better performance, giving the highest correct classification rate (CCR) of % for the valence dimension divided into three classes.

Depression is a common mental disorder, and in recent years, there has been an increasing trend of mild to moderate depression among college students. Additionally, effective detection of mild depression at an earlier stage remains an urgent problem that must be solved. In this study, electroencephalography (EEG) activities were recorded from 37 participants during processing of facial expression stimuli. With both high-gamma and low-gamma bands, the coherence in the right hemisphere of normal controls was greater than that of mildly depressive subjects, especially for electrodes P8, TP8, C4, FC4, and F8. In the low gamma band, the clustering coefficients of healthy controls in the prefrontal lobe (AF4, AFz, AF3, FC5, F4, and F6) and the parietal lobe (PO3, PO4, and P2) were significantly higher than those of mildly depressive subjects. The ratio of the characteristic path length between the functional network of the mildly depressed group and the small-world network was greater than 1. For the normal group, the ratio was near 1. This research contributes to the study of the cognitive process of mild depression. In our study, the results show closer cooperation in the brain areas of right hemisphere in normal controls during the cognitive process compared with the mildly depressed group, while the activity of the prefrontal and parietal regions in mild depression was significantly lower than that of normal controls. At the same time, in terms of the characteristic path length, the functional network of the mildly depressed group deviates from the small-world network.

Depression is a common mental disorder with growing prevalence, however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. Our study aims to develop an objective approach to depression detection that supports the process of diagnosis and assists the monitoring of risk factors. By classifying eye movement features during free viewing tasks, an accuracy of 80.1% was achieved using Random Forest to discriminate depressed and nondepressed subjects. Results indicate that eye movement features hold the potential to form a complimentary method of detection, having a relatively low computation overhead. Furthermore, given the proliferation of cheap internet eye movement detection technologies, the method offers the possibility of cost effective remote sensing of the patient mental state.

In an increasingly knowledge based world, people are confronted with an explosion of information from the environment which must be viewed in restricted attention spans. Hence there is a need to investigate how best to model our Visual Attention (VA) with a view to allocate our attention efficiently. We use the color-word Stroop task combined with electroencephalogram (EEG) to model VA: subjects undertake the Stroop task and their EEG is recorded. This is in contrast to other studies that use techniques such as Event Related Potentials (ERP), Contextual Modeling Frameworks, eye movements and facial recognition. The paper presents a simple and useful model to recognize VA dynamically. We use the linear EEG features of different cortical fields as the main inference factors, and take the response time (RT) of the Stroop task as a metric to quantify subject performance. First, we obtain the most relevant EEG feature vectors from the recording, using a correlation analysis. Second, we use experimental data for training the VA model, using a regression method. Last, we then apply further experimental data to test the proposed model. The results from the tests conducted demonstrate that our model maps visual attention very closely.