An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications.

Traditional advertising techniques seek to govern the consumer's opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers' actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

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.

Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies.

Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a seizure before it occurs may prove useful as patients might be alerted to make themselves safe or seizures could be prevented with therapeutic interventions just before they occur. Abnormal neuronal activity, the preictal state, starts a few minutes before the onset of a seizure. In recent years, different methods have been proposed to predict the start of the preictal state. These studies follow some common steps, including recording of EEG signals, preprocessing, feature extraction, classification, and postprocessing. However, online prediction of epileptic seizures remains a challenge as all these steps need further refinement to achieve high sensitivity and low false positive rate. In this paper, we present a comparison of state-of-the-art methods used to predict seizures using both scalp and intracranial EEG signals and suggest improvements to existing methods.

The clinical anatomy of fibromyalgia.

Fibromyalgia is a disorder characterized by pain and a spectrum of psychological comorbidities, rendering treatment difficult, and often a financial burden. Findings regarding diagnosis, prevalence, comorbidities, and potential pathophysiological links are discussed herein. Fibromyalgia is a complex disorder and there are specific criteria that patients must meet for diagnosis, including scores on fibromyalgia questionnaires, commonalities of age, gender, menopause status, sleep disturbances, and mood symptoms. The close relationship between fibromyalgia and other chronic disorders should alert the physician to explore for comorbid illnesses. In this review of the clinical anatomy of fibromyalgia, we review new studies that could be significant for the current use of clinical interventions for patients with symptoms. Using standard search engines, the clinical anatomy of fibromyalgia is investigated and many related studies are mentioned herein. Fibromyalgia is considered a prototypical central chronic pain syndrome and is associated with widespread pain that fluctuates spontaneously. There is also substantial lifetime psychiatric comorbidity in individuals with fibromyalgia, resulting in a low health-related quality of life. These results have important clinical and theoretical implications, including the possibility that fibromyalgia could share underlying pathophysiological links with some psychiatric disorders. This reveals that patients with fibromyalgia have findings compatible with tissue injury pain, the pain mechanisms involving both the primary afferent neuron and the nociceptive systems in the central nervous system. (1) There is a relationship between fibromyalgia and chronic disorders. This should alert the physician to explore for comorbid illnesses. (2) There is substantial lifetime psychiatric comorbidity resulting in a low health-related quality of life. (3) Patients with fibromyalgia have findings compatible with tissue injury pain Clin. Anat. 31:387-391, 2018. © 2018 Wiley Periodicals, Inc.

Neuroleukemiosis: Two Case Reports.

Extramedullary tumors composed of myeloblasts or monoblasts can present in various locations. Patients with a history of acute myeloid leukemia (AML) can present with neuropathic pain and no evidence of relapse of their leukemia. Neuroleukemiosis is a form of extramedullary tumor present in the peripheral nervous systems (PNS) of leukemia patients. We report two AML patients who were in remission and later presented with neurological symptoms due to neuroleukemiosis with negative bone marrow biopsies.

Bilateral Absence of the Zygomatic Nerve and Zygomaticofacial Nerve and Foramina.

The zygomaticofacial branch (ZFb) of the zygomatic nerve travels along the inferolateral angle of the orbit, traverses the zygomaticofacial foramen (ZFF) in the zygomatic bone, and then perforates the orbicularis oculi muscle to finally reach the skin of the malar area, which it innervates. The bilateral absence of the ZFb and the ZFF was found in an 80-year-old Caucasian cadaver. In addition, both zygomatic nerves were absent. A thin nerve arising from the lacrimal nerve passed below it and gave rise to the lacrimal branch and a communicating branch to the lacrimal nerve. This then entered the small bony canal, which opened at the medial aspect of the lateral wall of the orbit on the right and left sides. The bilateral absence of the ZFb of the zygomatic nerve and its foramen appears to be uncommon but should be realized during surgery or invasive procedures over the cheek or infraorbital region. The additional absence of both zygomatic nerves is exceptional.

Computer Based Melanocytic and Nevus Image Enhancement and Segmentation.

Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the most critical yet tricky part in computerized lesion detection and classification. The effectiveness and accuracy of lesion classification are critically dependent on the quality of lesion segmentation. In this paper, we have proposed a novel approach that can automatically preprocess the image and then segment the lesion. The system filters unwanted artifacts including hairs, gel, bubbles, and specular reflection. A novel approach is presented using the concept of wavelets for detection and inpainting the hairs present in the cancer images. The contrast of lesion with the skin is enhanced using adaptive sigmoidal function that takes care of the localized intensity distribution within a given lesion's images. We then present a segmentation approach to precisely segment the lesion from the background. The proposed approach is tested on the European database of dermoscopic images. Results are compared with the competitors to demonstrate the superiority of the suggested approach.

Segmentation of skin lesion using Cohen-Daubechies-Feauveau biorthogonal wavelet.

This paper presents a novel technique for segmentation of skin lesion in dermoscopic images based on wavelet transform along with morphological operations. The acquired dermoscopic images may include artifacts inform of gel, dense hairs and water bubble which make accurate segmentation more challenging. We have also embodied an efficient approach for artifacts removal and hair inpainting, to enhance the overall segmentation results. In proposed research, color space is also analyzed and selection of blue channel for lesion segmentation have confirmed better performance than techniques which utilizes gray scale conversion. We tackle the problem by finding the most suitable mother wavelet for skin lesion segmentation. The performance achieved with 'bior6.8' Cohen-Daubechies-Feauveau biorthogonal wavelet is found to be superior as compared to other wavelet family. The proposed methodology achieves 93.87 % accuracy on dermoscopic images of PH2 dataset acquired at Dermatology Service of Hospital Pedro Hispano, Matosinhos, Portugal.

Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces.

BACKGROUND AND OBJECTIVES: Macular diseases tend to damage macula within human retina due to which the central vision of a person is affected. Macular edema (ME) and central serous retinopathy (CSR) are two of the most common macular diseases. Many researchers worked on automated detection of ME from optical coherence tomography (OCT) and fundus images, whereas few researchers have worked on diagnosing central serous retinopathy. But this paper proposes a fully automated method for the classification of ME and CSR through robust reconstruction of 3D OCT retinal surfaces. METHODS: The proposed system uses structure tensors to extract retinal layers from OCT images. The 3D retinal surface is then reconstructed by extracting the brightness scan (B-scan) thickness profile from each coherent tensor. The proposed system extracts 8 distinct features (3 based on retinal thickness profile of right side, 3 based on thickness profile of left side and 2 based on top surface and cyst spaces within retinal layers) from 30 labeled volumes (10 healthy, 10 CSR and 10 ME) which are used to train the supervised support vector machines (SVM) classifier. RESULTS: In this research we have considered 90 OCT volumes (30 Healthy, 30 CSR and 30 ME) of 73 patients to test the proposed system where our proposed system correctly classified 89 out of 90 cases and has promising receiver operator characteristics (ROC) ratings with accuracy, sensitivity and specificity of 98.88%, 100%, and 96.66% respectively. CONCLUSION: The proposed system is quite fast and robust in detecting all the three types of retinal pathologies from volumetric OCT scans. The proposed system is fully automated and provides an early and on fly diagnosis of ME and CSR syndromes. 3D macular thickness surfaces can further be used as decision support parameter in clinical studies to check the volume of cyst.