Emotion recognition (ER) from Electroencephalogram (EEG) signals is a challenging task due to the non-linearity and non-stationarity nature of EEG signals. Existing feature extraction methods cannot extract the deep concealed characteristics of EEG signals from different layers for efficient classification scheme and also hard to select appropriate and effective feature extraction methods for different types of EEG data. Hence this study intends to develop an efficient deep feature extraction based method to automatically classify emotion status of people. In order to discover reliable deep features, five deep convolutional neural networks (CNN) models are considered: AlexNet, VGG16, ResNet50, SqueezeNet and MobilNetv2. Pre-processing, Wavelet Transform (WT), and Continuous Wavelet Transform (CWT) are employed to convert the EEG signals into EEG rhythm images then five well-known pretrained CNN models are employed for feature extraction. Finally, the proposed method puts the obtained features as input to the support vector machine (SVM) method for classifying them into binary emotion classes: valence and arousal classes. The DEAP dataset was used in experimental works. The experimental results demonstrate that the AlexNet features with Alpha rhythm produces better accuracy scores (91.07% in channel Oz) than the other deep features for the valence discrimination, and the MobilNetv2 features yields the highest accuracy score (98.93% in Delta rhythm (with channel C3) for arousal discrimination.

Machine learning has been densely used in most computer-aided medical diagnosis systems. These systems not only supported the physician’s decision but also accelerate the necessitated procedures. Electroencephalography (EEG) is an essential device for measuring the brain’s electrical activities. EEG is used to detect a series of brain disorders such as epilepsy, dementia, Parkinson’s disease, and Schizophrenia (SZ). In this work, a novel method for detecting SZ using EEG recordings is suggested. Initially, the presented technique breaks down each channel of the input EEG recordings into EEG rhythms. The wavelet transform is employed to achieve this. The 1D local binary pattern (LBP) is then used to code the acquired rhythm signals. Each row of the input picture is formed by concatenating the uniform histograms of the 1D LBP coded beats. The rows of the images are formed from the channels of the input EEG signal, while the columns of the images are constructed from the rhythms. Extreme learning machines (ELM) based autoencoders (AE) are utilized at a data augmentation step. After data augmentation, the SZ and healthy cases are classified using well-known deep transfer learning. Deep transfer learning employs a variety of pre-trained deep Convolutional Neural Network (CNN) models. Various performance assessment indicators are used to evaluate the produced outcomes. An EEG dataset that Lomonosov Moscow State University released is used in experiments, and a 97.7% accuracy score is obtained. The obtained results are also compared with several recently published methods. The comparisons show that the proposed method outperforms the compared methods.

We report the synthesis of Fe₃O₄/graphene (Fe₃O₄/Gr) nanocomposite for highly selective and highly sensitive peroxide sensor application. The nanocomposites were produced by a modified co-precipitation method. Further, structural, chemical, and morphological characterization of the Fe₃O₄/Gr was investigated by standard characterization techniques, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM) and high-resolution TEM (HRTEM), Fourier transform infrared (FTIR), and X-ray photoelectron spectroscopy (XPS). The average crystal size of Fe₃O₄ nanoparticles was calculated as 14.5 nm. Moreover, nanocomposite (Fe₃O₄/Gr) was employed to fabricate the flexible electrode using polymeric carbon fiber cloth or carbon cloth (pCFC or CC) as support. The electrochemical performance of as-fabricated Fe₃O₄/Gr/CC was evaluated toward H₂O₂ with excellent electrocatalytic activity. It was found that Fe₃O₄/Gr/CC-based electrodes show a good linear range, high sensitivity, and a low detection limit for H₂O₂ detection. The linear range for the optimized sensor was found to be in the range of 10–110 μM and limit of detection was calculated as 4.79 μM with a sensitivity of 0.037 µA μM⁻¹ cm⁻². The cost-effective materials used in this work as compared to noble metals provide satisfactory results. As well as showing high stability, the proposed biosensor is also highly reproducible.