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Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. Here, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders. Through simulations, we show that NNs leverage a neural 'laterality' dimension to distinguish between left and right-hand movements as neural tuning to both hands become increasingly correlated. In training recurrent neural networks (RNNs) for two-cursor control, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously. Our results suggest that neural network decoders may be advantageous for multi-effector decoding, provided they are designed to transfer to the online setting. [ABSTRACT FROM AUTHOR]

A Brain–Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain's compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain–computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system. [ABSTRACT FROM AUTHOR]

This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application. [ABSTRACT FROM AUTHOR]

Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs. Method: We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Results: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days). Conclusion: Experimental results demonstrate the superiority of DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work. [ABSTRACT FROM AUTHOR]

Reinforcement-learning (RL)-based brain-machine interfaces (BMIs) interpret dynamic neural activity into movement intention without patients’ real limb movements, which is promising for clinical applications. A movement task generally requires the subjects to reach the target within one step and rewards the subjects instantaneously. However, a real BMI scenario involves tasks that require multiple steps, during which sensory feedback is provided to indicate the status of the prosthesis, and the reward is only given at the end of the trial. Actually, subjects internally evaluate the sensory feedback to adjust motor activity. Existing RL-BMI tasks have not fully utilized the internal evaluation from the brain upon the sensory feedback to guide the decoder training, and there lacks an effective tool to assign credit for the multi-step decoding task. We propose first to extract intermediate guidance from the medial prefrontal cortex (mPFC) to assist the learning of multi-step decoding in an RL framework. To effectively explore the neural-action mapping in a large state-action space, a temporal difference (TD) method is incorporated into quantized attention-gated kernel reinforcement learning (QAGKRL) to assign the credit over the temporal sequence of movement, but also discriminate spatially in the Reproducing Kernel Hilbert Space (RKHS). We test our approach on the data collected from the primary motor cortex (M1) and the mPFC of rats when they brain control the cursor to reach the target within multiple steps. Compared with the models which only utilize the final reward, the intermediate evaluation interpreted from the mPFC can help improve the prediction accuracy by 10.9% on average across subjects, with faster convergence and more stability. Moreover, our proposed algorithm further increases 18.2% decoding accuracy compared with existing TD-RL methods. The results reveal the possibility of achieving better multi-step decoding performance for more complicated BMI tasks. [ABSTRACT FROM AUTHOR]

Scholars from various disciplines discuss the ethical, legal, and social implications of neurotechnology. Some have proposed four concrete "neurorights". This review presents the research of two pioneers in brain stimulation from the 1950s to 1970s, José M. R. Delgado and Elliot S. Valenstein, who also reflected upon the ethical, legal, and social aspects of their and other scientists' related research. Delgado even formulated the vision "toward a psychocivilized society" where brain stimulation is used to control, in particular, citizens' aggressive and violent behavior. Valenstein, by contrast, believed that the brain is not organized in such a way to allow the control or even removal of only negative processes without at the same time diminishing desirable ones. The paper also describes how animal and human experimentation on brain stimulation was carried out in that time period. It concludes with a contemporary perspective on the relevance of neurotechnology for neuroethics, neurolaw, and neurorights, including two recent examples for brain-computer interfaces. [ABSTRACT FROM AUTHOR]

Experiments with brain-machine interfaces (BMIs) reveal that the estimated preferred direction (EPD) of cortical motor units may shift following the transition to brain control. However, the cause of those shifts, and in particular, whether they imply neural adaptation, is an open issue. Here we address this question in simulations and theoretical analysis. Simulations are based on the assumption that the brain implements optimal state estimation and feedback control and that cortical motor neurons encode the estimated state and control vector. Our simulations successfully reproduce apparent shifts in EPDs observed in BMI experiments with different BMI filters, including linear, Kalman and re-calibrated Kalman filters, even with no neural adaptation. Theoretical analysis identifies the conditions for reducing those shifts. We demonstrate that simulations that better satisfy those conditions result in smaller shifts in EPDs. We conclude that the observed shifts in EPDs may result from experimental conditions, and in particular correlated velocities or tuning weights, even with no adaptation. Under the above assumptions, we show that if neurons are tuned differently to the estimated velocity, estimated position and control signal, the EPD with respect to actual velocity may not capture the real PD in which the neuron encodes the estimated velocity. Our investigation provides theoretical and simulation tools for better understanding shifts in EPD and BMI experiments. [ABSTRACT FROM AUTHOR]

A brain-controlled vehicle (BCV) is a vehicle that uses the Brain–Computer interface (BCI) to analyze the driver's electroencephalogram (EEG) to obtain human control commands, which can help disabled people extend movement range and improve their self-independence. However, the performance of BCV is poor and the accuracy of BCI recognition varies considerably with different subjects, which severely restrict its promotion and application. To develop a better solution, with the concept of shared control, an intention preferenced fusion brain control strategy based on fuzzy logic is proposed. Firstly, based on Fuzzy Discrete Event System Supervision (FDES) theory, instructions of the driver are supervised and evaluated. At the same time, based on fuzzy logic an automatic controller which gives an automatic decision according to the cur-rent state of the vehicle is designed. Then according to the automatic decision and the correctness of the driver's instructions, a fuzzy reasoning is made, making the final decision more in line with human intentions. In order to verify the effectiveness of the proposed method, based on our previously-designed SSMVEP platform, we conduct virtual experiments, semi-object tests and full physical tests separately for verification. Compared with others shared control schemes, it not only achieves continuous control, but the final control-making is more in line with human intentions, which can encourage users to become more involved, enhancing their self-esteem and brain functions. • An intention preferenced fusion brain control strategy is proposed. • FDES theory is used to supervise the brain instructions. • An automatic controller is designed based on fuzzy logic. • Fuzzy reasoning is used to make the final decision more in line with human intentions. • Virtual experiments, semi-object tests and full physical tests are conducted for verification. [ABSTRACT FROM AUTHOR]

Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to extend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data acquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate results are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired objectives and their real-time implementation as compared to other algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a brain–robot interface (BRI)-based control strategy in combination with the simultaneous localization and mapping (SLAM) to achieve the navigation and control of a mobile robot in uncertain environments. The BRI is based on steady state visually evoked potentials, utilizing the multivariate synchronization index classification algorithm to analyze the human electroencephalograph (EEG) signals in such a manner that human intentions can be recognized and motion commands can be produced for the brain controlled robot. The entire system is semi-autonomous since the navigation of mobile robot is commanded by the BRI, and the low-level motion of the mobile robot is autonomous with a designed kinematic controller. By utilizing vanishing points and door plates as the environmental features, a global metric map of the environment has been built by a sequential SLAM algorithm. The main contribution of this paper is the combination of an artificial potential field (APF) and the brain signals, which builds up the relationship between the strength of EEG signals and the intensity of the potential field. Through the proposed EEG-APF method, motion commands that would plan an obstacle-free trajectory in un-structured environments, can be obtained. The entire system has been tested with eight volunteer subjects, and all subjects are able to successfully fulfill manipulating mobile robot in the experiments. [ABSTRACT FROM AUTHOR]

In this study, a closed-loop brain stimulation control system scheme for epilepsy seizure abatement is designed by brain-machine interface (BMI) technique. In the controller design process, the practical parametric uncertainties involving cerebral blood flow, glucose metabolism, blood oxygen level dependence, and electromagnetic disturbances in signal control are considered. An appropriate transformation is introduced to express the system in regular form for design and analysis. Then, sufficient conditions are developed such that the sliding motion is asymptotically stable. Combining Caputo fractional order definition and neural network (NN), a finite time fractional order sliding mode (FFOSM) controller is designed to guarantee reachability of the sliding mode. The stability and reachability analysis of the closed-loop tracking control system gives the guideline of parameter selection, and simulation results based on comprehensive comparisons are carried out to demonstrate the effectiveness of proposed approach. [ABSTRACT FROM AUTHOR]

What can the brain–computer interface (BCI) do? Wearing an electroencephalogram (EEG) headcap, you can control the flight of a drone in the laboratory by your thought; with electrodes inserted inside the brain, paralytic patients can drink by controlling a robotic arm with thinking. Both invasive and non-invasive BCI try to connect human brains to machines. In the past several decades, BCI technology has continued to develop, making science fiction into reality and laboratory inventions into indispensable gadgets. In July 2019, Neuralink, a company founded by Elon Musk, proposed a sewing machine-like device that can dig holes in the skull and implant 3072 electrodes onto the cortex, promising more accurate reading of what you are thinking, although many serious scientists consider the claim misleading to the public. Recently, National Science Review (NSR) interviewed Professor Bin He, the department head of Biomedical Engineering at Carnegie Mellon University, and a leading scientist in the non-invasive-BCI field. His team developed new methods for non-invasive BCI to control drones by thoughts. In 2019, Bin's team demonstrated the control of a robotic arm to follow a continuously randomly moving target on the screen. In this interview, Bin He recounted the history of BCI, as well as the opportunities and challenges of non-invasive BCI. [ABSTRACT FROM AUTHOR]

To enhance the performance of the brain-actuated robot system, a novel shared controller based on Bayesian approach is proposed for intelligently combining robot automatic control and brain-actuated control, which takes into account the uncertainty of robot perception, action and human control. Based on maximum a posteriori probability (MAP), this method establishes the probabilistic models of human and robot control commands to realize the optimal control of a brain-actuated shared control system. Application on an intelligent Bayesian shared control system based on steady-state visual evoked potential (SSVEP)-based brain machine interface (BMI) is presented for all-time continuous wheelchair navigation task. Moreover, to obtain more accurate brain control commands for shared controller and adapt the proposed system to the uncertainty of electroencephalogram (EEG), a hierarchical brain control mechanism with feedback rule is designed. Experiments have been conducted to verify the proposed system in several scenarios. Eleven subjects participated in our experiments and the results illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Designing brain-computer interfaces (BCIs) that can be used in conjunction with ongoing motor behavior requires an understanding of how neural activity co-opted for brain control interacts with existing neural circuits. For example, BCIs may be used to regain lost motor function after stroke. This requires that neural activity controlling unaffected limbs is dissociated from activity controlling the BCI. In this study we investigated how primary motor cortex accomplishes simultaneous BCI control and motor control in a task that explicitly required both activities to be driven from the same brain region (i.e. a dual-control task). Single-unit activity was recorded from intracortical, multi-electrode arrays while a non-human primate performed this dual-control task. Compared to activity observed during naturalistic motor control, we found that both units used to drive the BCI directly (control units) and units that did not directly control the BCI (non-control units) significantly changed their tuning to wrist torque. Using a measure of effective connectivity, we observed that control units decrease their connectivity. Through an analysis of variance we found that the intrinsic variability of the control units has a significant effect on task proficiency. When this variance is accounted for, motor cortical activity is flexible enough to perform novel BCI tasks that require active decoupling of natural associations to wrist motion. This study provides insight into the neural activity that enables a dual-control brain-computer interface. [ABSTRACT FROM AUTHOR]

Brain Computer Interfaces (BCI) are devices that use brain signals for control or communication. Since they don't require movement of any part of the body, BCI are the natural choice for assisted communication when a person is unable to move. In this article, BCI based communicator for persons in locked-in state is described. It is based on P300 brain response of the user, thus does not require prior training, movement or imagination of movement. Auditory paradigm is selected in order to apply the communicator in cases where visual ability is also impaired. The communicator was designed to prove also whether low cost hardware with reduced electrode set could be used efficiently in everyday environment, without the need for expert personnel. The design of the communicator is described first, followed by detailed analyses of the performance when used by either healthy or disabled subjects. It is shown that auditory paradigm is the primary factor that limits the accuracy of communication. Hardware characteristics and reduced electrode set influence the accuracy in a negative way as well, while different questions and answer types produce no major differences. [ABSTRACT FROM AUTHOR]

Neuroprosthesis enables the brain control on the external devices purely using neural activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the discrepancy between the desired target and decoder’s output, where the correction may over-dominate the user’s intention. Reinforcement learning decoder allows users to actively adjust their brain patterns through trial and error, which better represents the subject’s motive. The computational challenge is to quickly establish new state-action mapping before the subject becomes frustrated. Recently proposed quantized attention-gated kernel reinforcement learning (QAGKRL) explores the optimal nonlinear neural-action mapping in the Reproducing Kernel Hilbert Space (RKHS). However, considering all past data in RKHS is less efficient and sensitive to detect the new neural patterns emerging in brain control. In this paper, we propose a clustering-based kernel RL algorithm. New neural patterns emerge and are clustered to represent the novel knowledge in brain control. The current neural data only activate the nearest subspace in RKHS for more efficient decoding. The dynamic clustering makes our algorithm more sensitive to new brain patterns. We test our algorithm on both the synthetic and real-world spike data. Compared with QAGKRL, our algorithm can achieve a quicker knowledge adaptation in brain control with less computational complexity. [ABSTRACT FROM AUTHOR]

Brain–machine interfaces (BMIs) translate brain signals into control signals for an external device, such as a computer cursor or robotic limb. These signals can be obtained either noninvasively or invasively. Invasive recordings, using electrocorticography (ECoG) or intracortical microelectrodes, provide higher bandwidth and more informative signals. Rehabilitative BMIs, which aim to drive plasticity in the brain to enhance recovery after brain injury, have almost exclusively used non-invasive recordings, such electroencephalography (EEG) or magnetoencephalography (MEG), which have limited bandwidth and information content. Invasive recordings provide more information and spatiotemporal resolution, but do incur risk, and thus are not usually investigated in people with stroke or traumatic brain injury (TBI). Here, in this paper, we describe a new BMI paradigm to investigate the use of higher frequency signals in brain-injured subjects without incurring significant risk. We recorded EEG in TBI subjects who required hemicraniectomies (removal of a part of the skull). EEG over the hemicraniectomy (hEEG) contained substantial information in the high gamma frequency range (65–115 Hz). Using this information, we decoded continuous finger flexion force with moderate to high accuracy (variance accounted for 0.06 to 0.52), which at best approaches that using epidural signals. These results indicate that people with hemicraniectomies can provide a useful resource for developing BMI therapies for the treatment of brain injury. [ABSTRACT FROM AUTHOR]

Directly using brain signals to drive a vehicle may not only help persons with disabilities to regain driving ability but also provide a new alternative way for healthy people to control a vehicle. In this paper, we propose a new longitudinal control system based on electroencephalogram signals for brain-controlled vehicles (BCVs) by combining a user interface, a brain-computer interface (BCI) system, and a longitudinal control module. Driver-in-the-loop experiments were conducted by using two driving tests (i.e., the destination-approaching and car-following tests) with different subjects under two control conditions, i.e., the brain and manual control conditions. Experimental results show the feasibility of alone using brain signals to continuously perform the longitudinal control of a vehicle at a relatively high speed, at least for some users. This paper not only promotes the development of BCVs but also provides some insights into the research on how to apply BCIs to control other high-speed dynamic systems. [ABSTRACT FROM AUTHOR]

Abstract: Recently, a new system called brain control system has been developed rapidly. Brain control system is a human-computer integration control system based on brain-computer interface (BCI), which relies on human's ideas and thinking. Brain control system has been successfully applied in wide fields, assisting disabled patients daily life, training patients with stroke or limb injury, monitoring the state of human operator, as well as entertainment and smart house etc. In this paper, the background, basic principle, system structure and developments are firstly introduced briefly. The current research status focusing on the problems of electroencephalograph (EEG) signal pattern, control signal transfer algorithm and system application is summarized and analyzed in detail. The further research direction and problems are discussed. Finally, the future development of brain control is analyzed and prospects are given. [ABSTRACT FROM AUTHOR]

Modern brain-machine interfaces can return function to people with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a non-prehensile task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined. During online brain control, the ReFIT Kalman filter could predict individuated finger group movements with high performance. Next, training ridge regression decoders with individual movements was sufficient to predict untrained combined movements and vice versa. Finally, we compared the postural and movement tuning of finger-related cortical activity to find that individual cortical units simultaneously encode multiple behavioral dimensions. Our results suggest that linear decoders may be sufficient for brain-machine interfaces to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses. [Display omitted] • Simultaneous and independent brain-machine interface control of two finger groups • Cortical tuning between manipulandum and brain-machine interface use is consistent • Linear decoders can predict untrained finger movements • Cortical units simultaneously encode multiple kinematic dimensions Nason et al. present a real-time brain-machine interface for controlling the simultaneous and independent movements of two groups of fingers in nonhuman primates. These techniques can be used to restore naturalistic control of paralyzed hands and enable a deeper understanding of how motor cortex represents dexterous finger behaviors. [ABSTRACT FROM AUTHOR]

A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications. [ABSTRACT FROM AUTHOR]

Time crystal was conceived in the 1970s as an autonomous engine made of only clocks to explain the life-like features of a virus. Later, time crystal was extended to living cells like neurons. The brain controls most biological clocks that regenerate the living cells continuously. Most cognitive tasks and learning in the brain run by periodic clock-like oscillations. Can we integrate all cognitive tasks in terms of running clocks of the hardware? Since the existing concept of time crystal has only one clock with a singularity point, we generalize the basic idea of time crystal so that we could bond many clocks in a 3D architecture. Harvesting inside phase singularity is the key. Since clocks reset continuously in the brain–body system, during reset, other clocks take over. So, we insert clock architecture inside singularity resembling brain components bottom-up and top-down. Instead of one clock, the time crystal turns to a composite, so it is poly-time crystal. We used century-old research on brain rhythms to compile the first hardware-free pure clock reconstruction of the human brain. Similar to the global effort on connectome, a spatial reconstruction of the brain, we advocate a global effort for more intricate mapping of all brain clocks, to fill missing links with respect to the brain's temporal map. Once made, reverse engineering the brain would remain a mere engineering challenge. [ABSTRACT FROM AUTHOR]

Brain–computer interface (BCI) is a technology used to convert brain signals to control external devices. Researchers have designed and built many interfaces and applications in the last couple of decades. BCI is used for prevention, detection, diagnosis, rehabilitation, and restoration in healthcare. EEG signals are analyzed in this paper to help paralyzed people in rehabilitation. The electroencephalogram (EEG) signals recorded from five healthy subjects are used in this study. The sensor level EEG signals are converted to source signals using the inverse problem solution. Then, the cortical sources are calculated using sLORETA methods at nine regions marked by a neurophysiologist. The features are extracted from cortical sources by using the common spatial pattern (CSP) method and classified by a support vector machine (SVM). Both the sensor and the computed cortical signals corresponding to motor imagery of the hand and foot are used to train the SVM algorithm. Then, the signals outside the training set are used to test the classification performance of the classifier. The 0.1–30 Hz and mu rhythm band-pass filtered activity is also analyzed for the EEG signals. The classification performance and recognition of the imagery improved up to 100% under some conditions for the cortical level. The cortical source signals at the regions contributing to motor commands are investigated and used to improve the classification of motor imagery. [ABSTRACT FROM AUTHOR]

The article discusses a research by M. Velliste and colleagues that demonstrates the use of brain-machine interfaces (BMI) technology by subjects to perform a practical behavirol act via brain control of the motion of a robotic arm in three-dimensional space. Using grids of fine electrodes implanted in the primary motor cortex of monkeys, the researchers trained the animals to generate patterns of brain activity to control an anthropomorphic robot that had a shoulder joint, an elbow joint and a claw-like gripper hand. They used standard operant conditioning methods, in which each successful reach, grasp and retrieval was reinforced by consumption of the food reward.