A Review of Emotion Recognition Using EEG Data and Machine Learning Techniques

Using AI to help humans with handling their emotions and identifying their stress levels in the current stressful lifestyle will greatly help them manage their lifestyle. Using the deep learning techniques, it can be made possible by creating a virtual bot to observe and understand human emotions.   In this paper, the researcher try to review the comments from Reddit that are used, preprocessed and trained using Deep Neural Network to learn the emotions of the user. The inference engine module, which is a hybrid network consisting of convolutional neural network and recurrent neural network, is also interfaced. The model provides a high accuracy of response. The selection of frequency bands plays an important role in discerning patterns of brain-related emotions. This document explores a new method for selecting appropriate thematic bands instead of using fixed bands to detect emotions. A common spatial technique and machine   machines were used to classify the emotional states.  This document describes a number of possible technologies aimed at communication and other applications; however, they represent only a small sample of the extensive future potential of these technologies.  We have also focused on relatively anticipated breakthroughs in the discussion of applications in sensory, BCI technologies; but breakthroughs like the new portable sensor technology, which offers ultra-high-resolution spatial and time-based activity in the brain, opens the door to a much broader range of applications. Keywords: Emotions, EEG, Machine Learning, Deep Learning, Systems and Signals DOI: 10.7176/ISDE/11-4-04 Publication date:August 31st 2020

Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection

Low-power sensing technologies, such as wearables, have emerged in the healthcare domain since they enable continuous and non-invasive monitoring of physiological signals. In order to endow such devices with clinical value, classical signal processing has encountered numerous challenges. However, data-driven methods, such as machine learning, offer attractive accuracies at the expense of being resource and memory demanding. In this paper, we focus on the inference of neural networks running in microcontrollers and low-power processors which wearable sensors and devices are generally equipped with. In particular, we adapted an existing convolutional-recurrent neural network, designed to detect and classify cardiac arrhythmias from a single-lead electrocardiogram, to the low-power embedded System-on-Chip nRF52 from Nordic Semiconductor with an ARM's Cortex-M4 processing core. We show our implementation in fixed-point precision, using the CMSIS-NN libraries, yields a drop of $F_1$ score from 0.8 to 0.784, from the original implementation, with a memory footprint of 195.6KB, and a throughput of 33.98MOps/s.Comment: Accepted for presentation in the 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS2020

Connectivity Analysis of Electroencephalograms in Epilepsy

This dissertation introduces a novel approach at gauging patterns of informa- tion flow using brain connectivity analysis and partial directed coherence (PDC) in epilepsy. The main objective of this dissertation is to assess the key characteristics that delineate neural activities obtained from patients with epilepsy, considering both focal and generalized seizures. The use of PDC analysis is noteworthy as it es- timates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and it ascertains the coefficients as weighted measures in formulating the multivariate autoregressive model (MVAR). The PDC is used here as a feature extraction method for recorded scalp electroencephalograms (EEG) as means to examine the interictal epileptiform discharges (IEDs) and reflect the phys- iological changes of brain activity during interictal periods. Two experiments were set up to investigate the epileptic data by using the PDC concept. For the investigation of IEDs data (interictal spike (IS), spike and slow wave com- plex (SSC), and repetitive spikes and slow wave complex (RSS)), the PDC analysis estimates the intensity and direction of propagation from neural activities gener- ated in the cerebral cortex, and analyzes the coefficients obtained from employing MVAR. Features extracted by using PDC were transformed into adjacency matrices using surrogate data analysis and were classified by using the multilayer Perceptron (MLP) neural network. The classification results yielded a high accuracy and pre- cision number. The second experiment introduces the investigation of intensity (or strength) of information flow. The inflow activity deemed significant and flowing from other regions into a specific region together with the outflow activity emanating from one region and spreading into other regions were calculated based on the PDC results and were quantified by the defined regions of interest. Three groups were considered for this study, the control population, patients with focal epilepsy, and patients with generalized epilepsy. A significant difference in inflow and outflow validated by the nonparametric Kruskal-Wallis test was observed for these groups. By taking advantage of directionality of brain connectivity and by extracting the intensity of information flow, specific patterns in different brain regions of interest between each data group can be revealed. This is rather important as researchers could then associate such patterns in context to the 3D source localization where seizures are thought to emanate in focal epilepsy. This research endeavor, given its generalized construct, can extend for the study of other neurological and neurode- generative disorders such as Parkinson, depression, Alzheimers disease, and mental illness

Ubiquitous computing: a learning system solution in the era of industry 4.0

Ubiquitous computing, which was initially advocated by Mark Weiser has become one of the keywords to express a vision of the near future of computing systems. The "ubiquitous world" is a ubiquitous computing environment with integrated networks; computer integrated manufacturing system (CIMS) and invisible computers which equipped sensor microchips and radio frequency identification systems. Anyone can access the ubiquitous computing systems anytime and anywhere broader, without individual awareness or skills. Ubiquitous computing is becoming crucial elements to organize the activities of groups of people by use of groupware under workforce mobility. The computer-supported cooperative work is transforming from telework to ubiquitous work with new information and communication technologies that support people working cooperatively. Ubiquitous learning is a demand for the knowledge workforce for more multi-skilled professionals. It is a new and emerging education and training system that integrating e-learning of cyberspace and mobile learning of physical space with a global repository that has the potential to be accessed by anyone at any place and anytime under ubiquitous integrated computing environment. In this paper, we discuss the study of emerging trends through the implementation of work and learning that influenced ubiquitous computing technology prospects. Furthermore, the perspective of ubiquitous work and learning system, gaining quality, and hence credibility with emerging information and communication technologies in education and training systems in the area of the education system are discussed. The experimental results showed that CIMS could improve the students learned more efficiently and achieved better learning performance

MyHealth: a cross-domain platform for healthcare

Health monitoring is changing the way people feel and care about their physical condition in an era where electronic devices and sensors can follow us in a continuous basis. This surveillance process is mainly related to very specific conditions or vital signs, being the collected information stored for later data processing. This paper presents the work undertaken under the central system of the MyHealth project, dedicated to the collection and analysis of information on physiological and hemostatic processes ensuring a source of integrated, flexible and shareable clinical information used to support the decision making process. The proposed system is able to collect and fuse data from different medical specialties, in different formats and with different data collection rates. The development of this work is based on advanced knowledge in the medical field, biomedical engineering, computing and telecommunications, thus benefitting from an interdisciplinary approach that is able to provide added value services and decision support information to the healthcare professionals.This project was funded by Fundo Europeu de Desenvolvimento Regional (FEDER), Programa Operacional Factores de Competitividade (POFC), Project number 13853, and was supported by FCT – Fundação para a Ciência e Tecnologia, within the Project Scope: PEst-OE/EEI/UI0319/2014

Graph neural networks for seizure discrimination based on electroencephalogram analysis

Este estudio presenta una investigación sobre la clasificación de Convulsiones Psicógenas No Epilépticas (PNES) y Convulsiones Epilépticas (ES) utilizando datos de EEG y Redes Neuronales de Grafos (GNN). El modelo propuesto muestra un rendimiento destacable, superando los resultados previos del estado del arte y logrando una precisión notable en la clasificación ternaria. Mediante el uso de una arquitectura GNN, el modelo distingue de manera efectiva entre PNES y ES con una precisión del 92.9%. Además, al emplear la validación cruzada "Leave One Group Out", el modelo logra una precisión aún mayor del 97.58%, superando la precisión más alta reportada en el estado del arte de 94.4%. Asimismo, al ampliar la clasificación para incluir a pacientes sanos, el modelo alcanza una precisión del 91.12%, superando la mejor precisión conocida del estado del arte de 85.7%. Estos hallazgos resaltan el potencial del modelo para clasificar y diferenciar de manera precisa estas condiciones médicas utilizando datos de EEG. El trabajo futuro incluye la exploración de biomarcadores para la clasificación binaria utilizando las capacidades de explicabilidad del modelo, contribuyendo al desarrollo de herramientas de diagnóstico objetivas y estrategias de tratamiento personalizadas. Además, este estudio compara el rendimiento, las metodologías y los conjuntos de datos de estudios similares del estado del arte, proporcionando una visión general completa de la investigación en clasificación de convulsiones. En conclusión, este estudio demuestra el éxito del modelo propuesto en la clasificación de PNES y ES, allanando el camino para futuros avances en el campo y beneficiando a pacientes y profesionales de la salud en el diagnóstico y tratamiento.This study presents a research investigation on the classification of Psychogenic Non-Epileptic Seizures (PNES) and Epileptic Seizures (ES) using EEG data and Graph Neural Networks (GNN). The proposed model demonstrates outstanding performance, surpassing previous state-of-the-art results and achieving remarkable accuracy in ternary classification. By utilizing a GNN architecture, the model effectively distinguishes between PNES and ES with an accuracy of 92.9%. Moreover, when employing Leave One Group Out crossvalidation, the model achieves an even higher accuracy of 97.58%, outperforming the highest reported state-of-the-art accuracy of 94.4%. Furthermore, by extending the classification to include healthy patients, the model achieves an accuracy of 91.12%, surpassing the bestknown state-of-the-art accuracy of 85.7%. These findings highlight the potential of the model in accurately classifying and differentiating these medical conditions using EEG data. Future work includes the exploration of biomarkers for binary classification using the model's explainability capabilities, contributing to the development of objective diagnostic tools and personalized treatment strategies. Additionally, this study compares the performance, methodologies, and datasets of similar studies from the state-of-the-art, providing a comprehensive overview of seizure classification research. In conclusion, this study demonstrates the success of the proposed model in classifying PNES and ES, paving the way for further advancements in the field and benefiting patients and healthcare practitioners in diagnosis and treatment

Swarm Intelligence Algorithm Based on Plant Root System in 1D Biomedical Signal Feature Engineering to Improve Classification Accuracy

The classification accuracy of one-dimensional (1D) biomedical signals is limited due to the lack of independence of the extracted features. To address this shortcoming, the study applies a swarm intelligence algorithm based on plant root systems (PRSs) to feature engineering. Some basic features of 1D biomedical signals are integrated into a digitized soil, and a root matrix is generated from this digitized soil and the PRS algorithm. The PRS features are extracted from the root matrix and used to classify the basic features. Following classification with the same biomedical signals and classifier, the accuracy of the added PRS set is generally higher than that of the base set. The result shows that the proposed algorithm can expand the application of 1D biomedical signals to include more biomedical signals in classification tasks for clinical diagnosis

Learning in the compressed data domain: Application to milk quality prediction

Smart dairy farming has become one of the most exciting and challenging area in cloud-based data analytics. Transfer of raw data from all farms to a central cloud is currently not feasible as applications are generating more data while internet connectivity is lacking in rural farms. As a solution, Fog computing has become a key factor to process data near the farm and derive farm insights by exchanging data between on-farm applications and transferring some data to the cloud. In this context, learning in the compressed data domain, where decompression is not necessary, is highly desirable as it minimizes the energy used for communication/computation, reduces required memory/storage, and improves application latency. Mid-infrared spectroscopy (MIRS) is used globally to predict several milk quality parameters as well as deriving many animal-level phenotypes. Therefore, compressed learning on MIRS data is beneficial both in terms of data processing in the Fog, as well as storing large data sets in the cloud. In this paper, we used principal component analysis and wavelet transform as two techniques for compressed learning to convert MIRS data into a compressed data domain. The study derives near lossless compression parameters for both techniques to transform MIRS data without impacting the prediction accuracy for a selection of milk quality traits