Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an electroencephalography (EEG)-based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM), the K nearest neighbor (KNN), the Naive Bayes (NB), the Decision Tree (DT), and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake and drowsy). Segmentation into 10 s intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data.

Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activity include interactions among unknown sources, low signal-to-noise ratio, and substantial between-subject heterogeneity. In this work, we propose a state space model that jointly analyzes multichannel EEG signals and learns dynamics of different sources corresponding to brain cortical activity. Our model borrows strength from spatially correlated measurements and uses low-dimensional latent states to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case-control study of alcoholism and reveal significant attenuation of brain activity in response to visual stimuli in alcoholic subjects compared to healthy controls.

Automated Sleep Stage Classification in Home Environments: An Evaluation of Seven Deep Neural Network Architectures.

Sleep is an essential human physiological need that has garnered increasing scientific attention due to the burgeoning prevalence of sleep-related disorders and their impact on public health. Among contemporary challenges, the demand for authentic sleep monitoring outside the confines of specialized laboratories, ideally within the home environment, has arisen. Addressing this, we explore the development of pragmatic approaches that facilitate implementation within domestic settings. Such approaches necessitate the deployment of streamlined, computationally efficient automated classifiers. In pursuit of a sleep stage classifier tailored for home use, this study rigorously assessed seven conventional neural network architectures prominent in deep learning (LeNet, ResNet, VGG, MLP, LSTM-CNN, LSTM, BLSTM). Leveraging sleep recordings from a cohort of 20 subjects, we elucidate that LeNet, VGG, and ResNet exhibit superior performance compared to recent advancements reported in the literature. Furthermore, a comprehensive architectural analysis was conducted, illuminating the strengths and limitations of each in the context of home-based sleep monitoring. Our findings distinctly identify LeNet as the most-amenable architecture for this purpose, with LSTM and BLSTM demonstrating relatively lesser compatibility. Ultimately, this research substantiates the feasibility of automating sleep stage classification employing lightweight neural networks, thereby accommodating scenarios with constrained computational resources. This advancement aims at revolutionizing the field of sleep monitoring, making it more accessible and reliable for individuals in their homes.

LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals.

Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam's spiral and Sophia Germain's prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time-frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time-frequency representation is saved as a time-frequency image, and a non-overlapping n × n sliding window is applied to this image for patch extraction. An n × n Ulam's spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain's primes are located in Ulam's spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children's neurological disorders.

Designing an Embedded Feature Selection Algorithm for a Drowsiness Detector Model Based on Electroencephalogram Data.

Driver fatigue reduces the safety of traditional driving and limits the widespread adoption of self-driving cars; hence, the monitoring and early detection of drivers' drowsiness plays a key role in driving automation. When representing the drowsiness indicators as large feature vectors, fitting a machine learning model to the problem becomes challenging, and the problem's perspicuity decreases, making dimensionality reduction crucial in practice. For this reason, we propose an embedded feature selection algorithm that can be later utilized as a building block in the system development of a neural network-based drowsiness detector. We have adopted a technique: a so-called Feature Prune Layer is placed in front of the first layer in the architecture; as a result, its weights change regarding the importance of the corresponding input features and are deleted iteratively until the desired number is reached. We test the algorithm on EEG data, as it is one of the best indicators of drowsiness based on the literature. The proposed FS algorithm is able to reduce the original feature set by 95% with only 1% degradation in precision, while the precision increases by 1.5% and 2.7% respectively when selecting the top 10% and top 20% of the initial features. Moreover, the proposed method outperforms the widely popular Principal Component Analysis and the Chi-squared test when reducing the original feature set by 95%: it achieves 24.3% and 3.2% higher precision respectively.

Image-Evoked Emotion Recognition for Hearing-Impaired Subjects with EEG Signals.

In recent years, there has been a growing interest in the study of emotion recognition through electroencephalogram (EEG) signals. One particular group of interest are individuals with hearing impairments, who may have a bias towards certain types of information when communicating with those in their environment. To address this, our study collected EEG signals from both hearing-impaired and non-hearing-impaired subjects while they viewed pictures of emotional faces for emotion recognition. Four kinds of feature matrices, symmetry difference, and symmetry quotient based on original signal and differential entropy (DE) were constructed, respectively, to extract the spatial domain information. The multi-axis self-attention classification model was proposed, which consists of local attention and global attention, combining the attention model with convolution through a novel architectural element for feature classification. Three-classification (positive, neutral, negative) and five-classification (happy, neutral, sad, angry, fearful) tasks of emotion recognition were carried out. The experimental results show that the proposed method is superior to the original feature method, and the multi-feature fusion achieved a good effect in both hearing-impaired and non-hearing-impaired subjects. The average classification accuracy for hearing-impaired subjects and non-hearing-impaired subjects was 70.2% (three-classification) and 50.15% (five-classification), and 72.05% (three-classification) and 51.53% (five-classification), respectively. In addition, by exploring the brain topography of different emotions, we found that the discriminative brain regions of the hearing-impaired subjects were also distributed in the parietal lobe, unlike those of the non-hearing-impaired subjects.

DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis.

Recent advancements in Electroencephalographic (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI) in several practical applications, ranging from rehabilitation systems to smart consumer technologies. When it comes to Signal Processing (SP) for BCI, there has been a surge of interest on Steady-State motion Visual Evoked Potentials (SSmVEP), where motion stimulation is used to address key issues associated with conventional light flashing/flickering. Such benefits, however, come with the price of being less accurate and having a lower Information Transfer Rate (ITR). From this perspective, this paper focuses on the design of a novel SSmVEP paradigm without using resources such as trial time, phase, and/or number of targets to enhance the ITR. The proposed design is based on the intuitively pleasing idea of integrating more than one motion within a single SSmVEP target stimuli, simultaneously. To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, called the Dual Frequency Aggregated Steady-State motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating "Radial Zoom" and "Rotation" motions in a single target without increasing the trial length. Compared to conventional SSmVEPs, the proposed DF-SSmVEP framework consists of two motion modes integrated and shown simultaneously each modulated by a specific target frequency. The paper also develops a specific unsupervised classification model, referred to as the Bifold Canonical Correlation Analysis (BCCA), based on two motion frequencies per target. The corresponding covariance coefficients are used as extra features improving the classification accuracy. The proposed DF-SSmVEP is evaluated based on a real EEG dataset and the results corroborate its superiority. The proposed DF-SSmVEP outperforms its counterparts and achieved an average ITR of 30.7 ± 1.97 and an average accuracy of 92.5 ± 2.04, while the Radial Zoom and Rotation result in average ITRs of 18.35 ± 1 and 20.52 ± 2.5, and average accuracies of 68.12 ± 3.5 and 77.5 ± 3.5, respectively.

EEG-Based Person Identification during Escalating Cognitive Load.

With the development of human society, there is an increasing importance for reliable person identification and authentication to protect a person's material and intellectual property. Person identification based on brain signals has captured substantial attention in recent years. These signals are characterized by original patterns for a specific person and are capable of providing security and privacy of an individual in biometric identification. This study presents a biometric identification method based on a novel paradigm with accrual cognitive brain load from relaxing with eyes closed to the end of a serious game, which includes three levels with increasing difficulty. The used database contains EEG data from 21 different subjects. Specific patterns of EEG signals are recognized in the time domain and classified using a 1D Convolutional Neural Network proposed in the MATLAB environment. The ability of person identification based on individual tasks corresponding to a given degree of load and their fusion are examined by 5-fold cross-validation. Final accuracies of more than 99% and 98% were achieved for individual tasks and task fusion, respectively. The reduction of EEG channels is also investigated. The results imply that this approach is suitable to real applications.

An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals.

Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods.

Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals.

Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with single-channel EEG signals for practical applications. The original sleep recordings were collected from the Sleep-EDF database. The wavelet threshold denoising (WTD) method and wavelet packet transformation (WPT) method were applied as signal preprocessing to extract six kinds of characteristic waves. With a comprehensive feature system including time, frequency, and nonlinear dynamics, we obtained the sleep stage classification results with different Support Vector Machine (SVM) models. We proposed a novel classification method based on cascaded SVM models with various features extracted from denoised EEG signals. To enhance the accuracy and generalization performance of this method, nonlinear dynamics features were taken into consideration. With nonlinear dynamics features included, the average classification accuracy was up to 88.11% using this method. In addition, with cascaded SVM models, the classification accuracy of the non-rapid eye movement sleep stage 1 (N1) was enhanced from 41.5% to 55.65% compared with the single SVM model, and the overall classification time for each epoch was less than 1.7 s. Moreover, we demonstrated that it was possible to apply this method for long-term sleep stage monitor applications.

Musical Emotions Recognition Using Entropy Features and Channel Optimization Based on EEG.

The dynamic of music is an important factor to arouse emotional experience, but current research mainly uses short-term artificial stimulus materials, which cannot effectively awaken complex emotions and reflect their dynamic brain response. In this paper, we used three long-term stimulus materials with many dynamic emotions inside: the "Waltz No. 2" containing pleasure and excitement, the "No. 14 Couplets" containing excitement, briskness, and nervousness, and the first movement of "Symphony No. 5 in C minor" containing passion, relaxation, cheerfulness, and nervousness. Approximate entropy (ApEn) and sample entropy (SampEn) were applied to extract the non-linear features of electroencephalogram (EEG) signals under long-term dynamic stimulation, and the K-Nearest Neighbor (KNN) method was used to recognize emotions. Further, a supervised feature vector dimensionality reduction method was proposed. Firstly, the optimal channel set for each subject was obtained by using a particle swarm optimization (PSO) algorithm, and then the number of times to select each channel in the optimal channel set of all subjects was counted. If the number was greater than or equal to the threshold, it was a common channel suitable for all subjects. The recognition results based on the optimal channel set demonstrated that each accuracy of two categories of emotions based on "Waltz No. 2" and three categories of emotions based on "No. 14 Couplets" was generally above 80%, respectively, and the recognition accuracy of four categories based on the first movement of "Symphony No. 5 in C minor" was about 70%. The recognition accuracy based on the common channel set was about 10% lower than that based on the optimal channel set, but not much different from that based on the whole channel set. This result suggested that the common channel could basically reflect the universal features of the whole subjects while realizing feature dimension reduction. The common channels were mainly distributed in the frontal lobe, central region, parietal lobe, occipital lobe, and temporal lobe. The channel number distributed in the frontal lobe was greater than the ones in other regions, indicating that the frontal lobe was the main emotional response region. Brain region topographic map based on the common channel set showed that there were differences in entropy intensity between different brain regions of the same emotion and the same brain region of different emotions. The number of times to select each channel in the optimal channel set of all 30 subjects showed that the principal component channels representing five brain regions were Fp1/F3 in the frontal lobe, CP5 in the central region, Pz in the parietal lobe, O2 in the occipital lobe, and T8 in the temporal lobe, respectively.

Stimulus transformation into motor action: Dynamic graph analysis reveals a posterior-to-anterior shift in brain network communication of older subjects.

Cognitive performance slows down with increasing age. This includes cognitive processes that are essential for the performance of a motor act, such as the slowing down in response to an external stimulus. The objective of this study was to identify aging-associated functional changes in the brain networks that are involved in the transformation of external stimuli into motor action. To investigate this topic, we employed dynamic graphs based on phase-locking of Electroencephalography signals recorded from healthy younger and older subjects while performing a simple visually-cued finger-tapping task. The network analysis yielded specific age-related network structures varying in time in the low frequencies (2-7 Hz), which are closely connected to stimulus processing, movement initiation and execution in both age groups. The networks in older subjects, however, contained several additional, particularly interhemispheric, connections and showed an overall increased coupling density. Cluster analyses revealed reduced variability of the subnetworks in older subjects, particularly during movement preparation. In younger subjects, occipital, parietal, sensorimotor and central regions were-temporally arranged in this order-heavily involved in hub nodes. Whereas in older subjects, a hub in frontal regions preceded the noticeably delayed occurrence of sensorimotor hubs, indicating different neural information processing in older subjects. All observed changes in brain network organization, which are based on neural synchronization in the low frequencies, provide a possible neural mechanism underlying previous fMRI data, which report an overactivation, especially in the prefrontal and pre-motor areas, associated with a loss of hemispheric lateralization in older subjects.

Exploring differences for motor imagery using Teager energy operator-based EEG microstate analyses.

In this paper, the differences between two motor imagery tasks are captured through microstate parameters (occurrence, duration and coverage, and mean spatial correlation (Mspatcorr)) derived from a novel method based on electroencephalogram microstate and Teager energy operator. The results show that the significance between microstate parameters for two tasks is different (P < 0.05) with paired t-test. Furthermore, these microstate parameters are utilized as features. Support vector machine is utilized to classify the two tasks with a mean accuracy of 93.93%, which yielded superior performance compared to the other methods.

EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels.

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognition based on EEG signals heavily relies on the features and their delineation, which requires the selection of feature categories converted from the raw signals and types of expressions that could display the intrinsic properties of an individual signal or a group of them. Moreover, the correlation or interaction among channels and frequency bands also contain crucial information for emotional state prediction, and it is commonly disregarded in conventional approaches. Therefore, in our method, the correlation between 32 channels and frequency bands were put into use to enhance the emotion prediction performance. The extracted features chosen from the time domain were arranged into feature-homogeneous matrices, with their positions following the corresponding electrodes placed on the scalp. Based on this 3D representation of EEG signals, the model must have the ability to learn the local and global patterns that describe the short and long-range relations of EEG channels, along with the embedded features. To deal with this problem, we proposed the 2D CNN with different kernel-size of convolutional layers assembled into a convolution block, combining features that were distributed in small and large regions. Ten-fold cross validation was conducted on the DEAP dataset to prove the effectiveness of our approach. We achieved the average accuracies of 98.27% and 98.36% for arousal and valence binary classification, respectively.

Convolutional Neural Network for Drowsiness Detection Using EEG Signals.

Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

Multiscale Permutation Lempel-Ziv Complexity Measure for Biomedical Signal Analysis: Interpretation and Application to Focal EEG Signals.

This paper analyses the complexity of electroencephalogram (EEG) signals in different temporal scales for the analysis and classification of focal and non-focal EEG signals. Futures from an original multiscale permutation Lempel-Ziv complexity measure (MPLZC) were obtained. MPLZC measure combines a multiscale structure, ordinal analysis, and permutation Lempel-Ziv complexity for quantifying the dynamic changes of an electroencephalogram (EEG). We also show the dependency of MPLZC on several straight-forward signal processing concepts, which appear in biomedical EEG activity via a set of synthetic signals. The main material of the study consists of EEG signals, which were obtained from the Bern-Barcelona EEG database. The signals were divided into two groups: focal EEG signals (n =100) and non-focal EEG signals (n = 100); statistical analysis was performed by means of non-parametric Mann-Whitney test. The mean value of MPLZC results in the non-focal group are significantly higher than those in the focal group for scales above 1 (p <0.05). The result indicates that the non-focal EEG signals are more complex. MPLZC feature sets are used for the least squares support vector machine (LS-SVM) classifier to classify into the focal and non-focal EEG signals. Our experimental results confirmed the usefulness of the MPLZC method for distinguishing focal and non-focal EEG signals with a classification accuracy of 86%.

A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG.

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer's disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.

[Audio-visual Emotion Regulation System Based on TGAM Module].

OBJECTIVE: Based on the TGAM PCB module, a system of emotion control using audio-visual feedback is designed. METHODS: TGAM collects EEG information through the electrode in contact with the forehead skin. The system analyzes the user's emotion through the STM32F103ZET6 of the main control chip, and finally controls the control end of the system to regulate the user's emotion. RESULTS: It can be seen from the test results that the system can precisely recognize the user's emotions, and at the same time effectively adjust the user's emotions from both audio-visual aspects. CONCLUSIONS: The system has high recognition accuracy and good adjustment effect.

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques.

Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accurate diagnosis and appropriate drug therapy to patients. This work aims to categorize electroencephalography signals on different channels' recordings for classifying and predicting epileptic seizures. The collection of the electroencephalography recordings contained in the dataset attributes 179 information and 11,500 instances. Instances are of five categories, where one is the symptoms of epilepsy seizure. We have used traditional, ensemble methods and deep machine learning techniques highlighting their performance for the epilepsy seizure detection task. One dimensional convolutional neural network, ensemble machine learning techniques like bagging, boosting (AdaBoost, gradient boosting, and XG boosting), and stacking is implemented. Traditional machine learning techniques such as decision tree, random forest, extra tree, ridge classifier, logistic regression, K-Nearest Neighbor, Naive Bayes (gaussian), and Kernel Support Vector Machine (polynomial, gaussian) are used for classifying and predicting epilepsy seizure. Before using ensemble and traditional techniques, we have preprocessed the data set using the Karl Pearson coefficient of correlation to eliminate irrelevant attributes. Further accuracy of classification and prediction of the classifiers are manipulated using k-fold cross-validation methods and represent the Receiver Operating Characteristic Area Under the Curve for each classifier. After sorting and comparing algorithms, we have found the convolutional neural network and extra tree bagging classifiers to have better performance than all other ensemble and traditional classifiers.

Statistical algorithms for emotion classification via functional connectivity.

Pattern recognition algorithms decode emotional brain states by using functional connectivity measures which are extracted from EEG signals as input to the statistical classifiers. An open-access EEG dataset for emotional state analysis is used to classify two dominant emotional models, based on valence and arousal. To calculate the functional connectivity between all available pairs of EEG electrodes four different measures, including Pearson's correlation coefficient, phase-locking value, mutual information, and magnitude square coherence estimation, were used. Three kinds of classifiers were applied to categorize single trials into two emotional states in each emotional model (high/low arousal, high/low valence). This procedure resulted in decoding performance of 68.30% and 60.33% for valence and arousal respectively in test trials which were significantly higher than chance (≈ 50%, t-test, and significance level of 0.05). The results obtained using a phase-locking value approach were significantly better than previous findings on the same data set. These results illustrate that functional connectivity between distinct neural populations can be considered as a neural coding mechanism for intrinsic emotional states.