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

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

A wheelchair-mounted robotic arm (WMRA) system was designed and built to meet the needs of mobilityimpaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The control of this 9-degree-of-freedom system expands upon conventional control methods and combines the 7-DoF robotic arm control with the 2-degree-of-freedom power wheelchair control. The 3- degrees of redundancy are optimized to effectively perform activities of daily living and overcome singularities, joint limits and some workspace limitations. The control system is designed for teleoperated or autonomous coordinated Cartesian control, which offers expandability for future research. A P300 Brain Computer Interface (BCI), the BCI2000, was implemented to control the WMRA system. The control is done by recording and analysing the brain activity through an electrode cap while providing visual stimulation to the user via a visual matrix. The visual matrix contains a symbolic or an alphabetic array corresponding to the motion of the WMRA. By recognizing online and in real-time, which element in the matrix elicited a P300, the BCI system can identify which element the user chose to communicate. The chosen element is then communicated to the controller of the WMRA system. The speed and accuracy of the BCI system was tested. This paper gives details of the WMRA's integration with the BCI2000 and documents the experimental results of the BCI and the WMRA in simulation.

Control of a 9-DoF Wheelchair-mounted robotic arm system using a P300 Brain Computer Interface: Initial experiments

We report the results of two experiments designed to clarify the spatial and temporal characteristics of the positive deflection that follows the error related negativity (ERN) elicited to incorrect responses in speeded reaction time tasks. Principal components analysis (PCA) indicates that the positive deflection reported to follow the ERN is composed of two different components: (a) a fronto-central positive deflection that follows the ERN and shares its spatial distribution and (b) a P300. When accuracy was required of the participants, the ERN and the P300 were larger in amplitude than when speed and accuracy were equally weighted. On the other hand, the amplitude of the fronto-central positive component was not affected by the degree to which accuracy was stressed.

Parsing the componential structure of post-error ERPs: a principal component analysis of ERPs following errors.

According to the reinforcement learning account of the error-related negativity ERN, the ERN is a manifestation of a signal generated in ACC as a consequence of a phasic decrease in the activity of the mesencephalic dopamine system occurring when the monitoring system evaluates events as worse than expected. This signal is also hypothesized to be used to modify behavior to ascertain that future events will have better outcomes. It is therefore expected that this signal be correlated with learning outcomes. We report a study designed to examine the extent to which the ERN is related to learning outcomes within a paired-associates learning task. The feedback-related negativity FRN elicited by stimuli that indicated to the participants whether their response was correct or not was examined both according the degree to which the associates were learned in the session and according to whether participants recalled the associations on the next day. The results of the spatio-temporal PCA indicate that, whereas the process giving rise to the negative feedback elicited a FRN whose amplitude was not correlated with long-term learning outcomes, positive feedback was associated with a FRN-like activity, which was correlated with the learning outcomes. Another ERP component that follows the FRN temporally and shares its spatial distribution was found associated with long-term learning outcomes. Our findings shed light on the functional significance of the feedback-related ERP components and are discussed within the framework of the reinforcement learning ERN hypothesis.

The good, the bad, or the useful? the examination of the relationship between the feedback-related negativity frn and long-term learning outcomes

A Brain Computer Interface (BCI) is a device that allows the user to communicate with the world without utilizing voluntary muscle activity (i.e., using only the electrical activity of the brain). It makes use of the well-studied observation that the brain reacts differently to different stimuli, as a function of the level of attention allotted to the stimulus stream and the specific processing triggered by the stimulus. In this article we present a single trial independent component analysis (ICA) method that is working with a BCI system proposed by Farwell and Donchin. It can dramatically reduce the signal processing time and improve the data communicating rate. This ICA method achieved 76.67% accuracy on single trial P300 response identification.

Yael Arbel

Papers

Control of a 9-DoF Wheelchair-mounted robotic arm system using a P300 Brain Computer Interface: Initial experiments

Parsing the componential structure of post-error ERPs: a principal component analysis of ERPs following errors.

The good, the bad, or the useful? the examination of the relationship between the feedback-related negativity frn and long-term learning outcomes

Single trial independent component analysis for P300 BCI system

BCIs that use P300 Event Related Potentials