Electroencephalography (EEG) remains an essential method for monitoring brain activity, but the limitations of conventional systems due to the complexity of installation and lack of portability have led to the introduction and development of in-ear EEG technology. In-ear EEG is an emerging method of recording electrical activity in the brain and is an innovative concept that offers multiple advantages both from the point of view of the device itself, which is easily portable, and from the user’s point of view, who is more comfortable with it, even in long-term use. One of the fundamental components of this type of device is the electrodes used to capture the EEG signal. This innovative method allows bioelectrical signals to be captured through electrodes integrated into an earpiece, offering significant advantages in terms of comfort, portability, and accessibility. Recent studies have demonstrated that in-ear EEG can record signals qualitatively comparable to scalp EEG, with an optimized signal-to-noise ratio and improved electrode stability. Furthermore, this review provides a comparative synthesis of performance parameters such as signal-to-noise ratio (SNR), common-mode rejection ratio (CMRR), signal amplitude, and comfort, highlighting the strengths and limitations of in-ear EEG systems relative to conventional scalp EEG. This study also introduces a visual model outlining the stages of technological development for in-ear EEG, from initial research to clinical and commercial deployment. Particular attention is given to current innovations in electrode materials and design strategies aimed at balancing biocompatibility, signal fidelity, and anatomical adaptability. This article analyzes the evolution of EEG in the ear, briefly presents the comparative aspects of EEG—EEG in the ear from the perspective of the electrodes used, highlighting the advantages and challenges of using this new technology. It also discusses aspects related to the electrodes used in EEG in the ear: types of electrodes used in EEG in the ear, improvement of contact impedance, and adaptability to the anatomical variability of the ear canal. A comparative analysis of electrode performance in terms of signal quality, long-term stability, and compatibility with use in daily life was also performed. The integration of intra-auricular EEG in wearable devices opens new perspectives for clinical applications, including sleep monitoring, epilepsy diagnosis, and brain–computer interfaces. This study highlights the challenges and prospects in the development of in-ear EEG electrodes, with a focus on integration into wearable devices and the use of biocompatible materials to improve durability and enhance user comfort. Despite its considerable potential, the widespread deployment of in-ear EEG faces challenges such as anatomical variability of the ear canal, optimization of ergonomics, and reduction in motion artifacts. Future research aims to improve device design for long-term monitoring, integrate advanced signal processing algorithms, and explore applications in neurorehabilitation and early diagnosis of neurodegenerative diseases.

A dangerous side effect of diabetes that can significantly lower quality of life and raise the death rate of diabetic individuals is diabetic autonomic neuropathy. It is essential to identify and anticipate this disease early on for prompt intervention and care. This study aims to predict this diabetic complication using Sudoscan and artificial intelligence. In this study, 172 individuals with type 1 or type 2 diabetes mellitus provided clinical and demographic information. Sudoscan was used to evaluate the subjects’ sudomotor dysfunction. Statistical methods were used to link various electrochemical skin conductance values with risk factors for neuropathy such as age, BMI, age of diabetes, or biochemical values such as cholesterol and triglycerides. Different machine-learning algorithms were used to predict the risk of diabetic autonomic neuropathy based on the collected data. The accuracy achieved with Logistic Regression is 92.6%, and with the Random Forest model is 96.3%. Lazzy Classifiers also show that six classifiers have a high performance of 97%. Thus, the use of machine learning algorithms in this field of metabolic diseases offers new perceptions for diagnosis, treatment, and prevention, and improves the quality of life of diabetic patients by reducing the incidence of complications related to this disease.

Humans are facing a devastating epidemic of metabolic syndrome that is linked to the worldwide dramatic increase in obesity and diabetes. Significant evidence suggests that the intestinal microbiota plays a major role in the pathogenesis of metabolic diseases. Due to the gut–brain axis link, dysbiosis in the gut microbiota have been demonstrated in both metabolic and neurological disease. Increasing evidence suggests that the gut microbiota is very important in maintaining health and changes in its composition may contribute to psychiatric and neurodegenerative disorders. It is also in research that changes in microbiota composition profile due to diabetes are modulated by the vagus nerve. Therefore, it is plausible that disruptions in the gut microbiota may be captured through electroencephalography signaling. Several studies which used standard methods of signal processing have highlighted some changes in electroencephalographic rhythms on patients with diabetes.

This paper describes the development and testing of a portable in-ear electroencephalography (EEG) system that uses dry electrodes made from different materials. Brain signals were collected from electrodes placed inside the ear and around the ear and were compared with signals recorded using standard scalp EEG systems. The results indicate that EEG signals recorded from the ear show a clear correlation with those obtained from scalp electrodes. At the same time, the in-ear system provides better user comfort, fewer movement-related artifacts, and a more discreet form factor. Even though the signal-to-noise ratio is slightly lower than in conventional EEG systems, the device remains suitable for portable and long-term recordings. These findings confirm that in-ear-EEG technology is a viable solution for applications in audiology, neuroscience, clinical brain monitoring, and brain–computer interface systems.

Counting bacterial colonies by hand is one of the most common tasks in microbiology, but it is slow and often yields different results depending on who does the counting. This paper presents an automated system for detecting and classifying bacterial colonies from Petri plate images. We tested four models: Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and a hybrid CNN+Vision Transformer (CNN+ViT). The system uses OpenCV for image preprocessing and extracts features such as area, perimeter, and circularity. Colonies are then classified by size and health condition. Confusion matrix results show that SVM and ANN achieved the highest overall accuracy (95.6%), while CNN+ViT eliminated false positives entirely. A Streamlit-based web interface allows real-time colony analysis directly in laboratory environments.