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.

/pdf/brain-computer-interfaces-a-review-2z42gnpdfy.pdf

Brain Computer Interfaces, a Review

Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.

https://iopscience.iop.org/article/10.1088/1741-2560/8/2/025005/pdf

Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general

Prediction during language comprehension: Benefits, costs, and ERP components

Successful goal-directed behavior requires not only correct action selection, planning, and execution but also the ability to flexibly adapt behavior when performance problems occur or the environment changes. A prerequisite for determining the necessity, type, and magnitude of adjustments is to continuously monitor the course and outcome of one's actions. Feedback-control loops correcting deviations from intended states constitute a basic functional principle of adaptation at all levels of the nervous system. Here, we review the neurophysiology of evaluating action course and outcome with respect to their valence, i.e., reward and punishment, and initiating short- and long-term adaptations, learning, and decisions. Based on studies in humans and other mammals, we outline the physiological principles of performance monitoring and subsequent cognitive, motivational, autonomic, and behavioral adaptation and link them to the underlying neuroanatomy, neurochemistry, psychological theories, and computational models. We provide an overview of invasive and noninvasive systemic measures, such as electrophysiological, neuroimaging, and lesion data. We describe how a wide network of brain areas encompassing frontal cortices, basal ganglia, thalamus, and monoaminergic brain stem nuclei detects and evaluates deviations of actual from predicted states indicating changed action costs or outcomes. This information is used to learn and update stimulus and action values, guide action selection, and recruit adaptive mechanisms that compensate errors and optimize goal achievement.

Neurophysiology of Performance Monitoring and Adaptive Behavior

While brain-computer interfaces (BCIs) can provide communication to people who are locked-in, they suffer from a very low information transfer rate. Further, using a BCI requires a concentration effort and using it continuously can be tiring. The brain controlled wheelchair (BCW) described in this paper aims at providing mobility to BCI users despite these limitations, in a safe and efficient way. Using a slow but reliable P300 based BCI, the user selects a destination amongst a list of predefined locations. While the wheelchair moves on virtual guiding paths ensuring smooth, safe, and predictable trajectories, the user can stop the wheelchair by using a faster BCI. Experiments with nondisabled subjects demonstrated the efficiency of this strategy. Brain control was not affected when the wheelchair was in motion, and the BCW enabled the users to move to various locations in less time and with significantly less control effort than other control strategies proposed in the literature.

/pdf/a-brain-controlled-wheelchair-to-navigate-in-familiar-5cre1p8c4w.pdf

A Brain Controlled Wheelchair to Navigate in Familiar Environments

We report the results of an experiment designed to elucidate the extent to which the error related ERP components are affected by response and stimulus similarity. We examined the ERPs under varying degrees of mismatch between the representations of actual and appropriate responses. We replicated the design used in an earlier study, which demonstrated that response similarity rather than stimulus similarity affected the amplitude of the error related negativity (ERN). We report the results of a spatial-temporal principal component analysis (PCA), which indicates that response similarity affects the amplitudes of the ERN and the fronto-central positive component, but not those of the P300 and the frontal negativity. The results provide evidence to suggest that the ERN and the proceeding positive deflection are error related and are sensitive to the degree of the committed error, whereas the P300 and the frontal negativity are not.

How large the sin? A study of the event related potentials elicited by errors of varying magnitude

https://digitalcommons.usf.edu/cgi/viewcontent.cgi?article=3403&context=psy_facpub

Developmental changes in the feedback related negativity from 8 to 14 years.

Three decades ago, Farwell and Donchin [1] developed a computer system based on the method of electroencephalography (EEG) that enables individuals to communicate with their environment without using any neuromuscular function. This P300 BCI speller makes use of the well-studied observation that the brain reacts differently to different stimuli, based on the level of attention given to the stimulus and the specific processing triggered by the stimulus. Since this first report in 1988, several brain-computer interface (BCI) systems have been developed and constantly improved. We have previously demonstrated that the P300-BCI can control a wheelchair-mounted robotic arm (WMRA) system [2].Copyright © 2008 by ASME

Implementation of a P-300 Brain Computer Interface for the Control of a Wheelchair Mounted Robotic Arm System

Effect of orthodontic treatment and comorbidity risk factors on interdental alveolar crest level: A radiographic evaluation

P300-Speller is one of the most practical and widely used Brain Computer Interface BCI for locked-in people who are not able to communicate with others via traditional communication methods. Many signal processing techniques have been utilized in P300-Speller to restore the communication ability of these locked-in people. These techniques are capable of achieving high classification accuracy. However the classification accuracy dramatically decreases for single trial analysis. The reason for that is that the noises existing in the recorded signals are usually removed by averaging several trials. When only a single trial is available, averaging is no longer an option for de-noising. The "averaging" step becomes the bottle neck of P300 response detection which highly limits the processing speed. Researchers are looking for techniques that can accomplish the classification task in a single trial. In this work, a new, effective but simple processing technique for single trial electroencephalography EEG classification using variance analysis based method is presented. This method achieved an overall accuracy of 84.8% for single trial P300 response identification. When compared with a single trial stepwise linear discriminant analysis SWLDA, the authors' method in terms of overall accuracy is more accurate and the data communication speed is significantly improved.

Yael Arbel

Papers

How large the sin? A study of the event related potentials elicited by errors of varying magnitude

Developmental changes in the feedback related negativity from 8 to 14 years.

Implementation of a P-300 Brain Computer Interface for the Control of a Wheelchair Mounted Robotic Arm System

Effect of orthodontic treatment and comorbidity risk factors on interdental alveolar crest level: A radiographic evaluation

A New Single Trial P300 Classification Method