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This study aims to conduct a systematic review of studies on neuroimaging measurements used in multimedia learning research. The particular aim of the review is to explore how cognitive processes i...

Empirical Mode Decomposition (EMD) provides an adaptive signal processing tool, and its multivariate extension is useful to model multichannel signals. Recently, EMD and multivariate EMD have succe...

Currently, Fourier-based, wavelet-based, and Hilbert-based time–frequency techniques have generated considerable interest in classification studies for emotion recognition in human–computer interface investigations. Empirical mode decomposition (EMD), one of the Hilbert-based time–frequency techniques, has been developed as a tool for adaptive signal processing. Additionally, the multi-variate version strongly influences designing the common oscillation structure of a multi-channel signal by utilizing the common instantaneous concepts of frequency and bandwidth. Additionally, electroencephalographic (EEG) signals are strongly preferred for comprehending emotion recognition perspectives in human–machine interactions. This study aims to herald an emotion detection design via EEG signal decomposition using multi-variate empirical mode decomposition (MEMD). For emotion recognition, the SJTU emotion EEG dataset (SEED) is classified using deep learning methods. Convolutional neural networks (AlexNet, DenseNet-201, ResNet-101, and ResNet50) and AutoKeras architectures are selected for image classification. The proposed framework reaches 99% and 100% classification accuracy when transfer learning methods and the AutoKeras method are used, respectively.

Meme kanseri, küresel olarak kadınlar arasında en çok görülen ve ölümle sonuçlanan kanser türleri arasındadır. Son yirmi yılda dünyanın farklı bölgelerinde yayınlanan epidemiyolojik çalışmalar, meme kanseri ölüm oranlarında önemli bir artış olduğunu göstermektedir. Bugün, mamografi meme dokusundaki kitlelerin ve mikrokalsifikasyonların görüntülenmesinde en etkin yöntemdir.. Öte yandan, mamografi tahmini ile yapılan meme biyopsileri, biyopsi olmadan önlenebilecek iyi huylu bulguların yaklaşık yüzde 70 kapsamaktadır. Bu nedenle mamografi analizi tahminlerinde hekimlere yardımcı olmak için otomatik bir yönteme ihtiyaç vardır. Araştırmacılar son yıllarda farklı medikal karar destek sistemleri önermiştir. Bu çalışmada, meme kanseri tanısı sürecinde kullanılacak bir tıbbi karar destek sistemi önerilmiştir. Bu sistemin temel amacı, gereksiz meme biyopsilerinin miktarını azaltmak ve tanıyı daha güvenilir hale getirmektir. Buna göre, tercih edilen mamografi özniteliklei içeren bir verikümesinde, hastanın yaşı dışında, meme dokusunun BI-RADS değerlendirmesi, kütlenin şekli, kütle payı, doku yoğunluğu, lezyonun ciddiyetini gösteren bir sınıf etiketi bir olasılıksal sınıflandırma algoritması olan Naive Bayes ve bir ileri beslemeli yapay sinir ağı olan Çok Katmanlı Perceptron algoritmalarının performansları değerlendirilmiştir. Önerilen sistem biyopsi veya kısa süreli takip kararı verilmesine yardımcı olabilir. Testsonuçları, önerilen yöntemin bilgisayar destekli tanı sistemlerinde bir karar modülü olarak kullanılabileceği konusunda umut vericidir., Breast cancer is the most widely recognized cancer-related death among women globally. Epidemiological studies released in different parts of the world over the past two decades show a significant rise in mortality rates for breast cancer. Today, mammography is the most effective method for imaging masses and microcalcifications in breast tissue. On the other hand, breast biopsy predictions arising from mammogram analysis lead to nearly 70 percent biopsies of benign findings that can be prevented without a biopsy. An automated method is therefore required to assist physicians in mammography analysis prognoses. Researchers have suggested different medical decision support systems recently. In this study, a medical decision support system to be utilized in the process of a breast cancer diagnosis is proposed. The primary purpose of this system is to lower the number of unnecessary breast biopsies and make the diagnosis more reliable. Accordingly, apart from the age of the patient, BI-RADS assessment of the breast tissue, the shape of the mass, mass margin, tissue density, the class label indicating the severity of the lesion are evaluated using the performances of a Naive Bayes algorithm , which is a probabilistic classification algorithm, and Multilayer Perceptron algorithm, which is a feed forward neural network, as two different classification algorithms via a preferred dataset in which each mammography mass have six different feature. The proposed system can help to make a biopsy or short-time follow-up decision. The test results are promising that the proposed method can be used as a decision module in computer-aided diagnosis systems.

© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.This study aimed to evaluate the split-attention effect in multimedia learning environments via objective measurements as EEG and eye-tracking. Two different multimedia learning environments in a focused (integrated) and split-attention (separated) format were designed. The experimental design method was used. The participants consisted of 44 students divided into two groups for focused attention and split-attention. There were significant differences between the fixation, brain wave, and retention performance of the two groups. Fixations of the split-attention group were higher than the focused attention group. A significant difference was found in the focused attention group in the alpha brain wave in the frontal region for intra-group comparisons and in the split-attention group in the beta brain wave in the frontal area for the inter-group comparison. The retention performance of the focused attention group was higher than the split-attention group. Accordingly, more cognitive activity emerged in environments where the text was not integrated into the picture. Additionally, the narration of text instead of printed text is effective for focusing attention. To prevent the emergence of a split-attention effect, the text should be integrated into the picture in designs. Due to the split-attention effect, the eye-tracking and EEG data were different between the groups.

Epilepsy is a persistent and recurring neurological condition in a community of brain neurons that results from sudden and abnormal electrical discharges. This paper introduces a new form of assessment and interpretation of the changes in electroencephalography (EEG) recordings from different brain regions in epilepsy disorders based on graph analysis and statistical rescale range analysis. In this study, two different states of epilepsy EEG data (preictal and ictal phases), obtained from 17 subjects (18 channels each), were analyzed by a new method called state transfer network (STN). The analysis performed by STN yields a network metric called motifs, which are averaged over all channels and subjects in terms of their persistence level in the network. The results showed an increase of overall motif persistence during the ictal over the preictal phase, reflecting the synchronization increase during the seizure phase (ictal). An evaluation of intermotif cross-correlation indicated a definite manifestation of such synchronization. Moreover, these findings are compared with several other well-known methods such as synchronization likelihood (SL), visibility graph similarity (VGS), and global field synchronization (GFS). It is hinted that the STN method is in good agreement with approaches in the literature and more efficient. The most significant contribution of this research is introducing a novel nonlinear analysis technique of generalized synchronization. The STN method can be used for classifying epileptic seizures based on the synchronization changes between multichannel data.

This research presents a new method for detecting obsessive–compulsive disorder (OCD) based on time–frequency analysis of multi-channel electroencephalogram (EEG) signals using the multi-variate synchrosqueezing transform (MSST). With the evolution of multi-channel sensor implementations, the employment of multi-channel techniques for the extraction of features arising from multi-channel dependency and mono-channel characteristics has become common. MSST has recently been proposed as a method for modeling the combined oscillatory mechanisms of multi-channel signals. It makes use of the concepts of instantaneous frequency (IF) and bandwidth. Electrophysiological data, like other nonstationary signals, necessitates both joint time–frequency analysis and independent time and frequency domain studies. The usefulness and effectiveness of a multi-variate, wavelet-based synchrosqueezing algorithm paired with a band extraction method are tested using electroencephalography data obtained from OCD patients and control groups in this research. The proposed methodology yields substantial results when analyzing differences between patient and control groups.

This paper presents a novel method for emotion recognition based on time-frequency analysis using multivariate synchrosqueezing transform (MSST) of multichannel electroencephalography (EEG) signals. With the advancements of the multichannel sensor applications, the need for multivariate algorithms has become obvious for extracting features that stem from multichannel dependency in addition to mono-channel features. In order to model the joint oscillatory structure of these multichannel signals, MSST has recently been proposed. It uses the concepts of joint instantaneous frequency and bandwidth. Electrophysiological data processing mostly requires joint time-frequency analysis in addition to both time and frequency analysis separately. The short-time Fourier transform (STFT) and wavelet transform (WT) are the main approaches utilized in time-frequency analysis. In this paper, the feasibility and performance of multivariate wavelet-based synchrosqueezing algorithm was demonstrated on EEG signals obtained from publically available DEAP database by comparing with its univariate version. Eight emotional states were considered by combining arousal-valence and dominance dimensions. Using linear support vector machines (SVM) as a classifier, MSST and its univariate version resulted in the highest prediction accuracy rates of ˜93% among all emotional states.

Obsessive–compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.

The aim of this study is to compare subjective and objective cognitive load measurements in a multimedia learning environment. For this purpose, 20 university students studied in multimedia environments designed by researchers during which eye movements and multichannel electroencephalography (EEG) signals were recorded. Self-report ratings were obtained at the end of the experiment, and retention performances of the students were measured. After the data were collected, Pearson Correlation analysis was applied. According to the results, significant relationship between the number of fixations and EEG frequency band powers was found. In addition, there was a negative relationship between retention performance and number of fixations. Moreover, a negative relationship was found between retention performance and self-reported measurements.

DOI: 10.26650/electrica.2018.00998 Emotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals.

In recent years, utilizing Hilbert-based time- frequency methods in emotional state sensing research attracted attention in the brain computer interfaces. Primarily, Hilbert Transform-based empirical mode decomposition (EMD) was found to be suitable for emotional state modeling studies. In more recent studies, models of emotional state recognition were proposed in which the classification was implemented by using the features obtained after applying the time, frequency, and time-frequency domain methods to intrinsic mode functions achieved by operating EMD. In this study, an analysis of emotional state recognition is proposed by using the features of the synchrosqueezing coefficients obtained in the classification process after applying the Synchrosqueezing Transform to intrinsic mode functions achieved by using Multivariate EMD. As a result, EEG data available in the DEAP database were categorized as low and high for valence, activation, and dominance dimensions, and 4 different classifiers were utilized in the classification process. The most satisfying ratios of valence, activation and dominance were attained 76%, 68%, and 68% respectively.

Emotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post-processing technique to compose a localized time-frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self-assessment-mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM.

In recent years, wavelet-based, Fourier-based and Hilbert-based time-frequency methods attracted attention in emotion state classification studies in human machine interaction. In particular, the Hilbert-based Empirical Mode Decomposition and Wavelet-based Discrete Wavelet Transform have found applications in emotional state analysis. In this study, a model of emotional elicitation is proposed in which the classification is made by using the features of the wavelet coefficients obtained after applying the Discrete Wavelet Transform to IMFs achieved by using Multivariate Empirical Mode Decomposition. Accordingly, EEG data available in the DEAP database were classified as low / high for valence, activation, and dominance dimensions, and 4 different classifiers were used in the classification phase. The best ratios of valence, activation and dominance were obtained ideally 70.1%, 58.8%, 60.3% respectively.

Electrophysiological data processing can take place both in time and in frequency domains as well as in the joint time-frequency domain. Short Time Fourier Transform and Wavelet Transform are commonly used time-frequency analysis methods. The limitations of these methods initiated the use of methods such as synchrosqueezing and multivariate synchrosqueezing methods. In our proposed method 88.9%, 77.8%, 80.6% accuracy rates were obtained respectively for the valence, activation and dominance parameters using and multivariate synchrosqueezing methods and support vector machines(SVM) which yields better results than most of the other methods mentioned in the literature. IEEE Turkey Sect

Stroke is one of the most significant public health problems. It is the third cause of mortality cause and first cause of paralysis in developed societies. Diagnosis of stroke is determined using clinical symptoms and several imaging modalities. Magnetic Resonance Imaging is one of these methods. Images obtained with these modalities are used by the physician on a routine clinical investigation. However, in some cases, the boundaries of the stroke tissue should be selected and mapped to the anatomical regions in the atlas. In this study, a graphical user interface is developed in order to identify and map the stroke regions to the digital anatomical atlas on registered and normalized MR images by SPM5. By using this interface, it is aimed to investigate the MR images of the stroke patients and perform specific therapy planning for different groups.

Music has a myriad of health-related benefits, both psychological and physiological. However, because the healthful effects of music have not been fully explored scientifically, many questions about the clinical efficacy of music are yet to be answered. In this study the musical selections used were reed flute sound and hard rock music. The information obtained from the present study adds significantly to what is known about the relaxing properties of music, by investigating how exposure to different styles of music the psychophysiological aspects of anxiety. The physiological measure was frontalis electromyography. EMG data taken from individual before music was listened, during listening to reed flute sound and during listening to hard rock music, were examined to extracted power spectral density by welch method which is use to evaluate unstable sign. Power spectral densities of during listening to reed flute and hard rock music were determined differently compared with psd of without music situation. As reed flute sound triggered off the decreased psd value, hard rock music triggered off the increased psd value. There is a reverse ratio between PSD on muscle and relief amount. In conclusion, listening to reed flute music has relaxing property according to hard rock music on frontal muscle.