Electrophysiological brain activity during the control of a motor imagery-based brain–computer interface

This article considers the features of five electroencephalogram patterns that are most frequently extracted by the independent component analysis when subjects imagine the movement of their hands during the control of a brain–computer interface (BCI). The solution of the EEG inverse problem using the individual geometrical head model shows that the sources of the revealed patterns are located at the bottom of the left and right central sulci, as well as in the left premotor cortex, supplementary motor area, and precuneus. The functional value of the patterns is discussed by comparing the location results with the results of the metaanalysis of the published data that were obtained using a functional magnetic resonance imaging. The source locations are the same for seven healthy subjects and four poststroke patients with subcortical damage location. However, despite the same locations, the two groups of subjects significantly differed in the frequency characteristics of the revealed patterns; in particular, the patients had no clearly pronounced activity in the upper α-band and were characterized by a much lower level of inhibition of rates in the primary somatosensory areas during motor imagery.     

Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control

Neuroprosthetic devices such as a computer cursor can be controlled by the activity of cortical neurons when an appropriate algorithm is used to decode motor intention. Algorithms which have been proposed for this purpose range from the simple population vector algorithm (PVA) and optimal linear estimator (OLE) to various versions of Bayesian decoders. Although Bayesian decoders typically provide the most accurate off-line reconstructions, it is not known which model assumptions in these algorithms are critical for improving decoding performance. Furthermore, it is not necessarily true that improvements (or deficits) in off-line reconstruction will translate into improvements (or deficits) in on-line control, as the subject might compensate for the specifics of the decoder in use at the time. Here we show that by comparing the performance of nine decoders, assumptions about uniformly distributed preferred directions and the way the cursor trajectories are smoothed have the most impact on decoder performance in off-line reconstruction, while assumptions about tuning curve linearity and spike count variance play relatively minor roles. In on-line control, subjects compensate for directional biases caused by non-uniformly distributed preferred directions, leaving cursor smoothing differences as the largest single algorithmic difference driving decoder performance.     

A comparison study of two P300 speller paradigms for brain–computer interface

In this paper, a comparison of two existing P300 spellers is conducted. In the first speller, the visual stimuli of characters are presented in a single character (SC) paradigm and each button corresponding to a character flashes individually in a random order. The second speller is based on a region-based (RB) paradigm. In the first level, all characters are grouped and each button corresponding to a group flashes individually in a random order. Once a group is selected, the characters in it will appear on the flashing buttons of the second level for the selection of desired character. In a spelling experiment involving 12 subjects, higher online accuracy was obtained on the RB paradigm-based P300 speller than the SC paradigm-based P300 speller. Furthermore, we analyzed P300 detection performance, the P300 waveforms and Fisher ratios using the data collected by the two spellers. It was found that the stimuli display paradigm of the RB speller enhances P300 potential and is more suitable for P300 detection.     

A frequency-weighted method combined with Common Spatial Patterns for electroencephalogram classification in brain–computer interface

Common Spatial Patterns (CSP) has been proven to be a powerful and successful method in the detection of event-related desynchronization (ERD) and ERD based brain–computer interface (BCI). However, frequency optimization combined with CSP has only been investigated by a few groups. In this paper, a frequency-weighted method (FWM) is proposed to optimize the frequency spectrum of surface electroencephalogram (EEG) signals for a two-class mental task classification. This straightforward method computes a weight value for each frequency component according to its importance for the discrimination task and reforms the spectrum with the computed weights. The off-line analysis shows that the proposed method achieves an improvement of about 4% (averaged over 24 datasets) in terms of cross-validation accuracy over the basic CSP.     

Artificial feedback for invasive brain–computer interfaces

During the last two decades, considerable progress has been made in the studies of brain–computer interfaces (BCIs)—devices in which motor signals from the brain are registered by multi-electrode arrays and transformed into commands for artificial actuators such as cursors and robotic devices. This review is focused on one problem. Voluntary motor control is based on neurophysiological processes, which strongly depend on the afferent innervation of skin, muscles, and joints. Thus, invasive BCI has to be based on a bidirectional system in which motor control signals are registered by multichannel microelectrodes implanted in motor areas, whereas tactile, proprioceptive, and other useful signals are transported back to the brain through spatiotemporal patterns of intracortical microstimulation (ICMS) delivered to sensory areas. In general, the studies of invasive BCIs have advanced in several directions. The progress of BCIs with artificial sensory feedback will not only help patients, but will also expand base knowledge in the field of human cortical functions.     

Effects of spectral smearing of stimuli on the performance of auditory steady-state response-based brain–computer interface

There have been few reports that investigated the effects of the degree and pattern of a spectral smearing of stimuli due to deteriorated hearing ability on the performance of auditory brain–computer interface (BCI) systems. In this study, we assumed that such spectral smearing of stimuli may affect the performance of an auditory steady-state response (ASSR)-based BCI system and performed subjective experiments using 10 normal-hearing subjects to verify this assumption. We constructed smearing-reflected stimuli using an 8-channel vocoder with moderate and severe hearing loss setups and, using these stimuli, performed subjective concentration tests with three symmetric and six asymmetric smearing patterns while recording electroencephalogram signals. Then, 56 ratio features were calculated from the recorded signals, and the accuracies of the BCI selections were calculated and compared. Experimental results demonstrated that (1) applying smearing-reflected stimuli decreases the performance of an ASSR-based auditory BCI system, and (2) such negative effects can be reduced by adjusting the feature settings of the BCI algorithm on the basis of results acquired a posteriori. These results imply that by fine-tuning the feature settings of the BCI algorithm according to the degree and pattern of hearing ability deterioration of the recipient, the clinical benefits of a BCI system can be improved.     

Sources of electrophysiological and foci of hemodynamic brain activity most relevant for controlling a hybrid brain–Computer interface based on classification of EEG patterns and near-infrared spectrography signals during motor imagery

A method is described for joint use of electroencephalography and near-infrared spectrography to locate sources of electrophysiological and foci of hemodynamic brain activity during motor execution and imagination. The sources of electrophysiological and foci of hemodynamic brain activity most relevant for controlling a hybrid brain-computer interface based on motor imagery are revealed and discussed.     

Recovery of the motor function of the arm with the aid of a hand exoskeleton controlled by a brain–computer interface in a patient with an extensive brain lesion

The dynamics of motor function recovery in a patient with an extensive brain lesion has been investigated during a course of neurorehabilitation assisted by a hand exoskeleton controlled by a brain–computer interface. Biomechanical analysis of the movements of the paretic arm recorded during the rehabilitation course was used for an unbiased assessment of motor function. Fifteen procedures involving hand exoskeleton control (one procedure per week) yielded the following results: (a) the velocity profile for targeted movements of the paretic hand became nearly bell-shaped; (b) the patient began to extend and abduct the hand, which was flexed and adducted at the beginning of the course; and (c) the patient started supinating the forearm, which was pronated at the beginning of the rehabilitation course. The first result is interpreted as improvement of the general level of control over the paretic hand, and the two other results are interpreted as a decrease in spasticity of the paretic hand.     

Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control

Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain–computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.     

Brain–computer interface: The first experience of clinical use in Russia

Motor imagery can stimulate the same neuroplastic mechanisms of the brain as their actual execution. The motor imagery can be controlled via the brain–computer interface (BCI), which transforms the EEG signals of the brain appearing during the motor imagery into commands for the external device. The results of the two-stage study of the application of a non-invasive BCI for the rehabilitation of patients with marked hemiparesis resulted from a local brain injury. We have shown that the learning to manage the BCI does not depend on the duration of disease, localization of the damaged site, and the severity of neurological deficit. The results of the first stage of the study carried out in a group of 36 patients showed that the rehabilitation therapy was more effective in the group that was trained to manage the BCI (the ARAT score improved from 1 [0; 2] to 5 [0; 16], p = 0.012 in the BCI group; no significant improvement was detected in the control group). In the second phase of the study, 19 patients participated in the testing of a BCI–exoskeleton system. Rehabilitation based on this technology led to an improvement of the motor function of an arm from 2 [0; 37] to 4 [1; 45.5], p = 0.005, according to the ARAT scale, and from 72 [63; 110] to 79 [68; 115], p = 0.005, according to the Fugl-Meyer scale.     

Insect–machine hybrid system for understanding and evaluating sensory-motor control by sex pheromone in Bombyx mori

To elucidate the dynamic information processing in a brain underlying adaptive behavior, it is necessary to understand the behavior and corresponding neural activities. This requires animals which have clear relationships between behavior and corresponding neural activities. Insects are precisely such animals and one of the adaptive behaviors of insects is high-accuracy odor source orientation. The most direct way to know the relationships between neural activity and behavior is by recording neural activities in a brain from freely behaving insects. There is also a method to give stimuli mimicking the natural environment to tethered insects allowing insects to walk or fly at the same position. In addition to these methods an ‘insect–machine hybrid system’ is proposed, which is another experimental system meeting the conditions necessary for approaching the dynamic processing in the brain of insects for generating adaptive behavior. This insect–machine hybrid system is an experimental system which has a mobile robot as its body. The robot is controlled by the insect through its behavior or the neural activities recorded from the brain. As we can arbitrarily control the motor output of the robot, we can intervene at the relationship between the insect and the environmental conditions.     

Cognitive brain–Computer interface and probable aspects of its practical application

A new type of brain-computer interface was elaborated. It considers a variety of brain activity parameters to determine the type of mental operation being performed at the moment. The corresponding algorithm previously developed in the lab was modified for real-time application. The possibility of interface application for cognitive skills training was investigated. In the proposed paradigm, as soon as the EEG spectral pattern was adequate for the current task, some clue to the solution was presented. As we supposed, such positive biofeedback should facilitate memorization of the current brain state. After just one learning session, the differences in EEG spectra, corresponding to two types of tasks, were concentrated in more narrow frequency ranges. It indicates a decrease in mental effort. Moreover, the majority of subjects succeeded in solving the tasks faster, which is evidence of increased efficiency. The developed interface could be used for the new type of training, based on objective features of brain activity.     

A reductionist approach to the analysis of learning in brain–computer interfaces

The complexity and scale of brain–computer interface (BCI) studies limit our ability to investigate how humans learn to use BCI systems. It also limits our capacity to develop adaptive algorithms needed to assist users with their control. Adaptive algorithm development is forced offline and typically uses static data sets. But this is a poor substitute for the online, dynamic environment where algorithms are ultimately deployed and interact with an adapting user. This work evaluates a paradigm that simulates the control problem faced by human subjects when controlling a BCI, but which avoids the many complications associated with full-scale BCI studies. Biological learners can be studied in a reductionist way as they solve BCI-like control problems, and machine learning algorithms can be developed and tested in closed loop with the subjects before being translated to full BCIs. The method is to map 19 joint angles of the hand (representing neural signals) to the position of a 2D cursor which must be piloted to displayed targets (a typical BCI task). An investigation is presented on how closely the joint angle method emulates BCI systems; a novel learning algorithm is evaluated, and a performance difference between genders is discussed.     

Fluid–structure interaction simulation of the brain–skull interface for acute subdural haematoma prediction

Traumatic brain injury is a leading cause of disability and mortality. Finite element-based head models are promising tools for enhanced head injury prediction, mitigation and prevention. The reliability of such models depends heavily on adequate representation of the brain–skull interaction. Nevertheless, the brain–skull interface has been largely simplified in previous three-dimensional head models without accounting for the fluid behaviour of the cerebrospinal fluid (CSF) and its mechanical interaction with the brain and skull. In this study, the brain–skull interface in a previously developed head model is modified as a fluid–structure interaction (FSI) approach, in which the CSF is treated on a moving mesh using an arbitrary Lagrangian–Eulerian multi-material formulation and the brain on a deformable mesh using a Lagrangian formulation. The modified model is validated against brain–skull relative displacement and intracranial pressure responses and subsequently imposed to an experimentally determined loading known to cause acute subdural haematoma (ASDH). Compared to the original model, the modified model achieves an improved validation performance in terms of brain–skull relative motion and is able to predict the occurrence of ASDH more accurately, indicating the superiority of the FSI approach for brain–skull interface modelling. The introduction of the FSI approach to represent the fluid behaviour of the CSF and its interaction with the brain and skull is crucial for more accurate head injury predictions.

     

Aging and urinary control: Alterations in the brain–bladder axis

Age-associated alterations in bladder control affect millions of older adults, with a heavy burden added to families both economically and in quality of life. Therapeutic options are limited with poor efficacy in older adults, lending to a growing need to address the gaps in our current understanding of urinary tract aging. This review summarizes the current knowledge of age-associated alterations in the structure and function of the brain–bladder axis and identifies important gaps in the field that have yet to be addressed. Urinary aging is associated with decreased tissue responsiveness, decreased control over the voiding reflex, signaling dysfunction along the brain–bladder axis, and structural changes within the bladder wall. Studies are needed to improve our understanding of how age affects the brain–bladder axis and identify genetic targets that correlate with functional outcomes.     

An ecological perspective of allelochemical interference in land–water interface communities

Allelochemical interactions among aquatic macrophytes and between macrophytes and attached microbial assemblages (epiphyton) influence a number of ecological processes. The ecological importance of these interactions, however, is poorly understood; we hypothesize that paucity has resulted, in part, from (1) a narrow focus on exploration for herbicidal plant products from aquatic macrophytes, (2) the difficulties in distinguishing resource competition from allelopathic interference, and (3) a predominance of approaching aquatic allelopathy from a terrestrial perspective. Based upon recent thorough investigations of allelopathy among aquatic vascular plants, chemical compounds that influence competitive interactions among littoral organisms are amphiphilic compounds that tend to remain near the producing organism (e.g., polyphenolic compounds and volatile fatty acids). Production of these compounds may be influenced by relative availability of nutrients (particularly phosphorus and nitrogen), inorganic carbon, and light. Macrophyte strategies of clonal reproduction, in an effort to persist in these highly productive and competitive habitats, have contributed to reduced reliance upon sexual reproduction that is correlated with allelopathic autotoxicity among several dominant wetland plant species. Although few studies document the importance of allelochemical interactions in the wetland and littoral zones of aquatic ecosystems, abundant evidence supports the potential for significant effects on competition and community structure; effects of altered nutrient ratios and availability on plant chemical composition; and resultant effects on trophic interactions, particularly suppression of herbivory, competitive attached algae and cyanobacteria, and heterotrophic utilization of organic matter by bacteria and fungi.     

Motor imaginary-based brain–machine interface design using programmable logic controllers for the disabled

Aiming at the implementation of brain–machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain's motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3 A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.