Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection

This article summarizes a systematic review of the electroencephalography (EEG)-based cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate experimental paradigms used for reliably eliciting discreet and quantifiable levels of cognitive load and the specific nature and representational structure of the commonly used input formulations in deep neural networks (DNNs) used for signal classification. The analysis revealed a number of studies using EEG signals in its native representation of a two-dimensional matrix for offline classification of CWL. However, only a few studies adopted an online or pseudo-online classification strategy for real-time CWL estimation. Further, only a couple of interpretable DNNs and a single generative model were employed for cognitive load detection till date during this review. More often than not, researchers were using DNNs as black-box type models. In conclusion, DNNs prove to be valuable tools for classifying EEG signals, primarily due to the substantial modeling power provided by the depth of their network architecture. It is further suggested that interpretable and explainable DNN models must be employed for cognitive workload estimation since existing methods are limited in the face of the non-stationary nature of the signal.Comment: 10 Pages, 4 figure

Mental State Prediction Using Machine Learning and EEG Signal

One of the most exciting areas of computer science right now is brain-computer interface (BCI) research. A conduit for data flow between both the brain as well as an electronic device is the brain-computer interface (BCI). Researchers in several disciplines have benefited from the advancements made possible by brain-computer interfaces. Primary fields of study include healthcare and neuroergonomics. Brain signals could be used in a variety of ways to improve healthcare at every stage, from diagnosis to rehabilitation to eventual restoration. In this research, we demonstrate how to classify EEG signals of brain waves using machine learning algorithms for predicting mental health states. The XGBoost algorithm's results have an accuracy of 99.62%, which is higher than that of any other study of its kind and the best result to date for diagnosing people's mental states from their EEG signals. This discovery will aid in taking efforts [1] to predict mental state using EEG signals to the next level

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but there is still significant progress attained in the last decade. Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups such as brain-computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning

Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI

There are significant milestones in modern human's civilization in which mankind stepped into a different level of life with a new spectrum of possibilities and comfort. From fire-lighting technology and wheeled wagons to writing, electricity and the Internet, each one changed our lives dramatically. In this paper, we take a deep look into the invasive Brain Machine Interface (BMI), an ambitious and cutting-edge technology which has the potential to be another important milestone in human civilization. Not only beneficial for patients with severe medical conditions, the invasive BMI technology can significantly impact different technologies and almost every aspect of human's life. We review the biological and engineering concepts that underpin the implementation of BMI applications. There are various essential techniques that are necessary for making invasive BMI applications a reality. We review these through providing an analysis of (i) possible applications of invasive BMI technology, (ii) the methods and devices for detecting and decoding brain signals, as well as (iii) possible options for stimulating signals into human's brain. Finally, we discuss the challenges and opportunities of invasive BMI for further development in the area.Comment: 51 pages, 14 figures, review articl

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others