Fractal-based classification of electroencephalography (EEG) signals in healthy adolescents and adolescents with symptoms of schizophrenia

Seizure detection using nonlinear measures over EEG frequency bands and deep learning classifiers

Epilepsy is a brain disorder that causes patients to suffer from convulsions, which affects their behavior and way of life. Epilepsy can be detected with electroencephalograms (EEGs), which record brain neural activity. Traditional approaches for detecting epileptic seizures from an EEG signal are time-consuming and annoying. To supersede these traditional methods, a myriad of automated seizure detection frameworks based on machine learning techniques have recently been developed. Feature extraction and classification are the two essential phases for seizure detection. The classifier assigns the proper class label after feature extraction lowers the input pattern space while maintaining useful features. This paper proposes a new feature extraction method based on calculating nonlinear features from the most relevant EEG frequency bands. The EEG signal is first decomposed into smaller time segments from which a vector of nonlinear features is computed and supplied to machine learning (ML) and deep learning (DL) classifiers. Experiments on the Bonn dataset reveals an accuracy of 99.7% reached in classifying normal and ictal EEG signals; and an accuracy of 98.8% in the discrimination of ictal and interictal EEG signals. Furthermore, a performance of 100% is achieved on the Hauz Khas dataset. The classification results of the proposed approach were compared to those from published state of the art techniques. Our results are equivalent to or better than some recent studies appeared in the literature.

SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals

Objective.Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging.Approach.The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model.Results.The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model.Significance.The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios.

Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals

Schizophrenia is a chronic mental disorder that impairs a person's thinking capacity, feelings and emotions, behavioural traits, etc., Emotional distortions, delusions, hallucinations, and incoherent speech are all some of the symptoms of schizophrenia, and cause disruption of routine activities. Computer-assisted diagnosis of schizophrenia is significantly needed to give its patients a higher quality of life. Hence, an improved adaptive neuro-fuzzy inference system based on the Hybrid Grey Wolf-Bat Algorithm for accurate prediction of schizophrenia from multi-channel EEG signals is presented in this study. The EEG signals are pre-processed using a Butterworth band pass filter and wICA initially, from which statistical, time-domain, frequency-domain, and spectral features are extracted. Discriminating features are selected using the ReliefF algorithm and are then forwarded to ANFIS for classification into either schizophrenic or normal. ANFIS is optimized by the Hybrid Grey Wolf-Bat Algorithm (HWBO) for better efficiency. The method is experimented on two separate EEG datasets-1 and 2, demonstrating an accuracy of 99.54% and 99.35%, respectively, with appreciable F1-score and MCC. Further experiments reveal the efficiency of the Hybrid Wolf-Bat algorithm in optimizing the ANFIS parameters when compared with traditional ANFIS model and other proven algorithms like genetic algorithm-ANFIS, particle optimization-ANFIS, crow search optimization algorithm-ANFIS and ant colony optimization algorithm-ANFIS, showing high R2 value and low RSME value. To provide a bias free classification, tenfold cross validation is performed which produced an accuracy of 97.8% and 98.5% on the two datasets respectively. Experimental outcomes demonstrate the superiority of the Hybrid Grey Wolf-Bat Algorithm over the similar techniques in predicting schizophrenia.

Decoding the correlation between heart activation and walking path by information-based analysis

BACKGROND

One of the important areas of heart research is to analyze heart rate variability during (HRV) walking.

OBJECTIVE

In this research, we investigated the correction between heart activation and the variations of walking paths.

METHOD

We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation.

RESULTS

According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series.

CONCLUSION

The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.

Applying machine learning techniques to detect the deployment of spatial working memory from the spiking activity of MT neurons

Neural signatures of working memory have been frequently identified in the spiking activity of different brain areas. However, some studies reported no memory-related change in the spiking activity of the middle temporal (MT) area in the visual cortex. However, recently it was shown that the content of working memory is reflected as an increase in the dimensionality of the average spiking activity of the MT neurons. This study aimed to find the features that can reveal memory-related changes with the help of machine-learning algorithms. In this regard, different linear and nonlinear features were obtained from the neuronal spiking activity during the presence and absence of working memory. To select the optimum features, the Genetic algorithm, Particle Swarm Optimization, and Ant Colony Optimization methods were employed. The classification was performed using the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) classifiers. Our results suggest that the deployment of spatial working memory can be perfectly detected from spiking patterns of MT neurons with an accuracy of 99.65±0.12 using the KNN and 99.50±0.26 using the SVM classifiers.

Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles

BACKGROUND

Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis.

METHOD

We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. Deep-spindle was trained on the EEGs of ex-term infants to estimate the number and duration of sleep spindles. The ex-term EEG on channel F4-C4 was split into training (N=81) and validation (N=30) sets. An additional 30 ex-term EEG and 54 ex-preterm infant EEGs (channel F4-C4 and F3-C3) were used as an independent test set.

RESULT

Deep-spindle detected the number of sleep spindles with 91.9% to 96.5% sensitivity and 95.3% to 96.7% specificity, and estimated sleep spindle duration with a percent error of 13.1% to 19.1% in the independent test set. For each detected spindle event, the user is presented with amplitude, power spectral density and the spectrogram of the corresponding spindle EEG, and the probability of the event being a sleep spindle event, providing the user with insight into why the event is predicted as a sleep spindle to provide confidence in the predictions.

CONCLUSION

The Deep-spindle system can reduce physicians' workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.

A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification

The vital data about the electrical activities of the brain are carried by the electroencephalography (EEG) signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, EEG studies incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model (FHM) with Singular Value Decomposition (SVD) Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee (HDPAB) algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model (ZIPMRM) and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset.

A self-learned decomposition and classification model for schizophrenia diagnosis

BACKGROUND

Schizophrenia (SZ) is a type of neurological disorder that is diagnosed by professional psychiatrists based on interviews and manual screening of patients. The procedures are time-consuming, burdensome, and prone to human error. This urgently necessitates the development of an effective and precise computer-aided design for the detection of SZ. One such efficient source for SZ detection is the electroencephalogram (EEG) signals. Because EEG signals are non-stationary, it is challenging to find representative information in its raw form. Decomposing the signals into multi-modes can provide detailed insight information from it. But the choice of uniform decomposition and hyper-parameters leads to information loss affecting system performance drastically.

METHOD

In this paper, automatic signal decomposition and classification methods are proposed for the detection of SZ and healthy control EEG signals. The Fisher score method is used for the selection of the most discriminant channel. Flexible tunable Q wavelet transform (F-TQWT) is developed for efficient decomposition of EEG signals by reducing root mean square error with grey wolf optimization (GWO) algorithm. Five features are extracted from the adaptively generated subbands and selected by the Kruskal Wallis test. The feature matrix is given as an input to the flexible least square support vector machine (F-LSSVM) classifier. The hyper-parameters and kernel of classifier are selected such that the accuracy of each subband is maximized using GWO algorithm.

RESULTS

The effectiveness and superiority of the proposed method is tested by evaluating seven performance parameters. An accuracy of 91.39%, sensitivity, specificity, precision, F-1 measure, false positive rate and error of 92.65%, 93.22%, 95.57%, 0.9306, 6.78% and 8.61% is achieved. The results prove superiority of the developed F-TQWT decomposition and F-LSSVM classifier over existing methodologies.

CONCLUSION

The EEG signals of healthy control and SZ subjects performing motor and auditory tasks simultaneously provide higher discrimination ability over the subjects performing auditory and motory tasks separately. The developed model is accurate, robust, and effective as it is developed on a relatively larger data-set, obtained maximum performance, and tested using ten-fold cross-validation technique. This proposed model is ready to be put to test for real-time SZ detection.

Information-based analysis of the relation between human muscle reaction and walking path

BACKGROUND

Walking is one of the important actions of the human body. For this purpose, the human brain communicates with leg muscles through the nervous system. Based on the walking path, leg muscles act differently. Therefore, there should be a relation between the activity of leg muscles and the path of movement.

OBJECTIVE

In order to address this issue, we analyzed how leg muscle activity is related to the variations of the path of movement.

METHOD

Since the electromyography (EMG) signal is a feature of muscle activity and the movement path has complex structures, we used entropy analysis in order to link their structures. The Shannon entropy of EMG signal and walking path are computed to relate their information content.

RESULTS

Based on the obtained results, walking on a path with greater information content causes greater information content in the EMG signal which is supported by statistical analysis results. This allowed us to analyze the relation between muscle activity and walking path.

CONCLUSION

The method of analysis employed in this research can be applied to investigate the relation between brain or heart reactions and walking path.

Information-based decoding of the coupling among human brain activity and movement paths

BACKGROUND

Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements.

OBJECTIVE

This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view.

METHODS

We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents.

RESULTS

According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

Analysis of the information transfer between brains during a conversation

BACKGROUND: The interaction between people is one of the usual daily activities. For this purpose, people mainly connect with others, using their voice. Voices act as the auditory stimuli on the brain during a conversation. OBJECTIVE: In this research, we analyze the relationship between the brains’ activities of subjects during a conversation. METHODS: Since human voice transfers information from one subject to another, we used information theory for our analysis. We investigated the alterations of Shannon entropy of electroencephalography (EEG) signals for subjects during a conversation. RESULTS: The results demonstrated that the alterations in the information contents of the EEG signals for the listeners and speakers are correlated. Therefore, we concluded that the brains’ activities of both subjects are linked. CONCLUSION: Our results can be expanded to analyze the coupling among other physiological signals of subjects (such as heart rate) during the conversation.

A hybrid deep neural network for classification of schizophrenia using EEG Data

Schizophrenia is a serious mental illness that causes great harm to patients, so timely and accurate detection is essential. This study aimed to identify a better feature to represent electroencephalography (EEG) signals and improve the classification accuracy of patients with schizophrenia and healthy controls by using EEG signals. Our research method involves two steps. First, the EEG time series is preprocessed, and the extracted time-domain and frequency-domain features are transformed into a sequence of red-green-blue (RGB) images that carry spatial information. Second, we construct hybrid deep neural networks (DNNs) that combine convolution neural networks and long short-term memory to address RGB images to classify schizophrenic patients and healthy controls. The results show that the fuzzy entropy (FuzzyEn) feature is more significant than the fast Fourier transform (FFT) feature in brain topography. The deep learning (DL) method that we propose achieves an average accuracy of 99.22% with FuzzyEn and an average accuracy of 96.34% with FFT. These results show that the best effect is to extract fuzzy features as input features from EEG time series and then use a hybrid DNN for classification. Compared with the most advanced methods in this field, significant improvements have been achieved.

Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuli

BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles. OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain. METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content). RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals. CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.

Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing

One of the serious mental disorders where people interpret reality in an abnormal state is schizophrenia. A combination of extremely disordered thinking, delusion, and hallucination is caused due to schizophrenia, and the daily functions of a person are severely disturbed because of this disorder. A wide range of problems are caused due to schizophrenia such as disturbed thinking and behaviour. In the field of human neuroscience, the analysis of brain activity is quite an important research area. For general cognitive activity analysis, electroencephalography (EEG) signals are widely used as a low-resolution diagnosis tool. The EEG signals are a great boon to understand the abnormality of the brain disorders, especially schizophrenia. In this work, schizophrenia EEG signal classification is performed wherein, initially, features such as Detrend Fluctuation Analysis (DFA), Hurst Exponent, Recurrence Quantification Analysis (RQA), Sample Entropy, Fractal Dimension (FD), Kolmogorov Complexity, Hjorth exponent, Lempel Ziv Complexity (LZC), and Largest Lyapunov Exponent (LLE) are extracted initially. The extracted features are, then, optimized for selecting the best features through four types of optimization algorithms here such as Artificial Flora (AF) optimization, Glowworm Search (GS) optimization, Black Hole (BH) optimization, and Monkey Search (MS) optimization, and finally, it is classified through certain classifiers. The best results show that, for normal cases, a classification accuracy of 87.54% is obtained when BH optimization is utilized with Support Vector Machine-Radial Basis Function (SVM-RBF) kernel, and for schizophrenia cases, a classification accuracy of 92.17% is obtained when BH optimization is utilized with SVM-RBF kernel.

Complexity-based decoding of the relation between human voice and brain activity

BACKGROUND: The human voice is the main feature of human communication. It is known that the brain controls the human voice. Therefore, there should be a relation between the characteristics of voice and brain activity. OBJECTIVE: In this research, electroencephalography (EEG) as the feature of brain activity and voice signals were simultaneously analyzed. METHOD: For this purpose, we changed the activity of the human brain by applying different odours and simultaneously recorded their voices and EEG signals while they read a text. For the analysis, we used the fractal theory that deals with the complexity of objects. The fractal dimension of EEG signal versus voice signal in different levels of brain activity were computed and analyzed. RESULTS: The results indicate that the activity of human voice is related to brain activity, where the variations of the complexity of EEG signal are linked to the variations of the complexity of voice signal. In addition, the EEG and voice signal complexities are related to the molecular complexity of applied odours. CONCLUSION: The employed method of analysis in this research can be widely applied to other physiological signals in order to relate the activities of different organs of human such as the heart to the activity of his brain.

Physiological State and Learning Ability of Students in Normal and Virtual Reality Conditions: Complexity-Based Analysis

Background

Education and learning are the most important goals of all universities. For this purpose, lecturers use various tools to grab the attention of students and improve their learning ability. Virtual reality refers to the subjective sensory experience of being immersed in a computer-mediated world, and has recently been implemented in learning environments.

Objective

The aim of this study was to analyze the effect of a virtual reality condition on students’ learning ability and physiological state.

Methods

Students were shown 6 sets of videos (3 videos in a two-dimensional condition and 3 videos in a three-dimensional condition), and their learning ability was analyzed based on a subsequent questionnaire. In addition, we analyzed the reaction of the brain and facial muscles of the students during both the two-dimensional and three-dimensional viewing conditions and used fractal theory to investigate their attention to the videos.

Results

The learning ability of students was increased in the three-dimensional condition compared to that in the two-dimensional condition. In addition, analysis of physiological signals showed that students paid more attention to the three-dimensional videos.

Conclusions

A virtual reality condition has a greater effect on enhancing the learning ability of students. The analytical approach of this study can be further extended to evaluate other physiological signals of subjects in a virtual reality condition.

Complexity-based decoding of brain-skin relation in response to olfactory stimuli

BACKGROUND AND OBJECTIVE

Human body is covered with skin in different parts. In fact, skin reacts to different changes around human. For instance, when the surrounding temperature changes, human skin will react differently. It is known that the activity of skin is regulated by human brain. In this research, for the first time we investigate the relation between the activities of human skin and brain by mathematical analysis of Galvanic Skin Response (GSR) and Electroencephalography (EEG) signals.

METHOD

For this purpose, we employ fractal theory and analyze the variations of fractal dimension of GSR and EEG signals when subjects are exposed to different olfactory stimuli in the form of pleasant odors.

RESULTS

Based on the obtained results, the complexity of GSR signal changes with the complexity of EEG signal in case of different stimuli, where by increasing the molecular complexity of olfactory stimuli, the complexity of EEG and GSR signals increases. The results of statistical analysis showed the significant effect of stimulation on variations of complexity of GSR signal. In addition, based on effect size analysis, fourth odor with greatest molecular complexity had the greatest effect on variations of complexity of EEG and GSR signals.

CONCLUSION

Therefore, it can be said that human skin reaction changes with the variations in the activity of human brain. The result of analysis in this research can be further used to make a model between the activities of human skin and brain that will enable us to predict skin reaction to different stimuli.