Brain computer interface: control signals review

People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain-computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future.

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EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 st Century.

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

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Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide a comprehensive survey of the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.

https://iopscience.iop.org/article/10.1088/1741-2552/abc902/pdf

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.

Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.

Deep Learning in the Biomedical Applications: Recent and Future Status

Brain computer interfacing: Applications and challenges

The Bluetooth Low Energy (BLE) is an attractive solution for implementing low-cost, low power consumption, short-range wireless transmission technology and high flexibility wireless products, which working on standard coin-cell batteries for years. The original design of BLE is restricted to star topology networking, which limits network coverage and scalability. In contrast, other competing technologies like Wi-Fi and ZigBee overcome those constraints by supporting different topologies such as the tree and mesh network topologies. This paper presents a part of the researchers’ efforts in designing solutions to enable BLE mesh networks and implements a tree network topology which is not supported in the standard BLE specifications. In addition, it discusses the advantages and drawbacks of the existing BLE network solutions. During analyzing the existing solutions, we highlight currently open issues such as flooding-based and routing-based solutions to allow end-to-end data transmission in a BLE mesh network and connecting BLE devices to the internet to support the Internet of Things (IoT). The approach proposed in this paper combines the default BLE star topology with the flooding based mesh topology to create a new hybrid network topology. The proposed approach can extend the network coverage without using any routing protocol.

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Networking solutions for connecting bluetooth low energy devices - a comparison

This paper presents an authentication system that uses brain waves as a biometric discriminant trait. It utilizes Electroencephalogram EEG signals generated from mental writing of the user-owned password. Independent Component Analysis ICA and baseline correction has been used for preprocessing and noise removal. The effect of two types of features, multivariate autoregressive MVAR model parameters and power spectral density PSD features, have been studied for this activity. Performance results based on single trial analysis have revealed that imagined password writing can reach average Half Total Error Rate HTER of 5i¾ź% for PSD features vs 3i¾ź% obtained with MVAR coefficients. The experiments have shown that mental password writing can be used for increasing the user acceptance for enrollment conditions while maintaining high performance results.

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Single Trial Authentication with Mental Password Writing

Identity manipulation is considered a serious security issue that has been enlarged with the spread of automated systems that could be accessed either locally or remotely. Availability, integrity, and confidentiality represent the basic requirements that should be granted for successful authentication systems. Personality verification has taken multiple forms depending on different possession types. They are divided into knowledge based, token based, and biometric based authentication. The permanent ownership to the human being has increased the chances of deploying biometrics based authentication in highly secure systems. It includes capturing the biological traits, which are physiological or behavioral, extracting the important features and comparing them to the previously stored features that belong to the claimed user. Various kinds of attacks aim to take down the basic requirements at multiple points. This paper describes different types of authentication along with their vulnerable points and threatening attacks. Then it provides more details about the biometric system structure as well as examples of distinguishing biological characteristics, organized by their locations. It shows the performance results of various biometric systems along with the deployed algorithms for different components.

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Authentication systems: principles and threats

During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99.8%. The system accuracy has been compared to other commonly used classifiers such as decision tree classifier, Gaussian Naïve Bayes and neural network which give precision of 98.4%, 92.6%, and 62.7% respectively. The experiments’ accuracy results show the possibility of distinguishing fake news and giving credibility scores for social media news with a relatively high performance.

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Sarah N. Abdulkader

Papers

Brain computer interfacing: Applications and challenges

Networking solutions for connecting bluetooth low energy devices - a comparison

Single Trial Authentication with Mental Password Writing

Authentication systems: principles and threats

Automated detection of fake news