A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

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Brain Computer Interfaces, a Review

Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.

Brain–computer interfaces for communication and rehabilitation

Objective. At the balanced intersection of human and machine adaptation is found the optimally functioning brain–computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s−1. Significance. Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain–computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

https://iopscience.iop.org/article/10.1088/1741-2560/10/4/046003/pdf

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface

Nearly half a century ago, telemedicine was disregarded for being an unwieldy, unreliable, and unaffordable technology. Rapidly evolving telecommunications and information technologies have provided a solid foundation for telemedicine as a feasible, dependable, and useful technology. Practitioners from a variety of medical specialties have claimed success in their telemedicine pursuits. Gradually, this new modality of healthcare delivery is finding its way into the mainstream medicine. As a multidisciplinary, dynamic, and continually evolving tool in medicine, researchers and users have developed various definitions for telemedicine. The meaning of telemedicine encapsulated in these definitions varies with the context in which the term was applied. An analysis of these definitions can play an important role in improving understanding about telemedicine. In this paper we present an extensive literature review that produced 104 peer-reviewed definitions of telemedicine. These definitions have been analyzed to highlight the context in which the term has been defined. The paper also suggests a definition of modern telemedicine. The authors suggest that telemedicine is a branch of e-health that uses communications networks for delivery of healthcare services and medical education from one geographical location to another. It is deployed to overcome issues like uneven distribution and shortage of infrastructural and human resources. We expect that this study will enhance the level of understanding and meaning of telemedicine among stakeholders, new entrants, and researchers, eventually enabling a better quality of life.

What is telemedicine? A collection of 104 peer-reviewed perspectives and theoretical underpinnings.

Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

A comprehensive review of EEG-based brain-computer interface paradigms.

This study aims to propose an effective and practical paradigm for a brain-computer interface (BCI)-based 2-D virtual wheelchair control. The paradigm was based on the multi-class discrimination of spatiotemporally distinguishable phenomenon of event-related desynchronization/synchronization (ERD/ERS) in electroencephalogram signals associated with motor execution/imagery of right/left hand movement. Comparing with traditional method using ERD only, where bilateral ERDs appear during left/right hand mental tasks, the 2-D control exhibited high accuracy within a short time, as incorporating ERS into the paradigm hypothetically enhanced the spatiotemporal feature contrast of ERS versus ERD. We also expected users to experience ease of control by including a noncontrol state. In this study, the control command was sent discretely whereas the virtual wheelchair was moving continuously. We tested five healthy subjects in a single visit with two sessions, i.e., motor execution and motor imagery. Each session included a 20 min calibration and two sets of games that were less than 30 min. Average target hit rate was as high as 98.4% with motor imagery. Every subject achieved 100% hit rate in the second set of wheelchair control games. The average time to hit a target 10 m away was about 59 s, with 39 s for the best set. The superior control performance in subjects without intensive BCI training suggested a practical wheelchair control paradigm for BCI users.

Electroencephalography (EEG)-Based Brain–Computer Interface (BCI): A 2-D Virtual Wheelchair Control Based on Event-Related Desynchronization/Synchronization and State Control

Prediction of human voluntary movement before it occurs.

Towards a User-Friendly Brain-Computer Interface: Initial Tests in ALS and PLS Patients

This study aims to explore whether human intentions to move or cease to move right and left hands can be decoded from spatiotemporal features in non-invasive EEG in order to control a discrete two-dimensional cursor movement for a potential multidimensional brain-computer interface (BCI). Five naive subjects performed either sustaining or stopping a motor task with time locking to a predefined time window by using motor execution with physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored. The performance of the proposed BCI was evaluated by both offline classification and online two-dimensional cursor control. Event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were observed on the contralateral hemisphere to the hand moved for both motor execution and motor imagery. Feature analysis showed that EEG beta band activity in the contralateral hemisphere over the motor cortex provided the best detection of either sustained or ceased movement of the right or left hand. The offline classification of four motor tasks (sustain or cease to move right or left hand) provided 10-fold cross-validation accuracy as high as 88% for motor execution and 73% for motor imagery. The subjects participating in experiments with physical movement were able to complete the online game with motor execution at an average accuracy of 85.5 +/- 4.65%; the subjects participating in motor imagery study also completed the game successfully. The proposed BCI provides a new practical multidimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training.

https://iopscience.iop.org/article/10.1088/1741-2560/6/4/046005/pdf

Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control.

The VitalPoll Telemedicine System (VTS) was designed and developed for wireless home healthcare. The aims of this study were: to design the architecture and communication methods for a telemedicine system; to implement a physiologic routing hub to collect data from different medical devices and sensors; and to evaluate the feasibility of this system for applications in wireless home healthcare. The VTS was built using Bluetooth wireless and Internet technologies with client/server architecture. Several medical devices, which acquire vital signs, such as real-time electrocardiogram signals, heart rate, body temperature, and activity (physical motion), were integrated into the VTS. Medical information and data were transmitted over short-range interface (USB, RS232), wireless communication, and the Internet. The medical results were stored in a database and presented using a web browser. The patient's vital signals can be collected, transmitted, and displayed in real time by the VTS. The experiments verified no data loss during Bluetooth and Internet communication. Bluetooth and the Internet provide enough bandwidth channels to tranmit these vital signs. The experimental results show that VTS may be suitable for a practical telemedicine system in home healthcare.

Ding-Yu Fei

Papers

Electroencephalography (EEG)-Based Brain–Computer Interface (BCI): A 2-D Virtual Wheelchair Control Based on Event-Related Desynchronization/Synchronization and State Control

Prediction of human voluntary movement before it occurs.

Towards a User-Friendly Brain-Computer Interface: Initial Tests in ALS and PLS Patients

Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control.

A telemedicine system for wireless home healthcare based on Bluetooth and the Internet.