Motor imagery-induced EEG patterns in individuals with spinal cord injury and their impact on brain–computer interface accuracy

Objective. Assimilating the diagnosis complete spinal cord injury (SCI) takes time and is not easy, as patients know that there is no ‘cure’ at the present time. Brain–computer interfaces (BCIs) can facilitate daily living. However, inter-subject variability demands measurements with potential user groups and an understanding of how they differ to healthy users BCIs are more commonly tested with. Thus, a three-class motor imagery (MI) screening (left hand, right hand, feet) was performed with a group of 10 able-bodied and 16 complete spinal-cord-injured people (paraplegics, tetraplegics) with the objective of determining what differences were present between the user groups and how they would impact upon the ability of these user groups to interact with a BCI. Approach. Electrophysiological differences between patient groups and healthy users are measured in terms of sensorimotor rhythm deflections from baseline during MI, electroencephalogram microstate scalp maps and strengths of inter-channel phase synchronization. Additionally, using a common spatial pattern algorithm and a linear discriminant analysis classifier, the classification accuracy was calculated and compared between groups. Main results. It is seen that both patient groups (tetraplegic and paraplegic) have some significant differences in event-related desynchronization strengths, exhibit significant increases in synchronization and reach significantly lower accuracies (mean (M) = 66.1%) than the group of healthy subjects (M = 85.1%). Significance. The results demonstrate significant differences in electrophysiological correlates of motor control between healthy individuals and those individuals who stand to benefit most from BCI technology (individuals with SCI). They highlight the difficulty in directly translating results from healthy subjects to participants with SCI and the challenges that, therefore, arise in providing BCIs to such individuals

Motor imagery induced EEG patterns in spinal cord injury patients and their impact on Brain-Computer Interface accuracy

OBJECTIVE: Assimilating the diagnosis complete spinal cord injury (SCI) takes time and is not easy, as patients know that there is no 'cure' at the present time. Brain-computer interfaces (BCIs) can facilitate daily living. However, inter-subject variability demands measurements with potential user groups and an understanding of how they differ to healthy users BCIs are more commonly tested with. Thus, a three-class motor imagery (MI) screening (left hand, right hand, feet) was performed with a group of 10 able-bodied and 16 complete spinal-cord-injured people (paraplegics, tetraplegics) with the objective of determining what differences were present between the user groups and how they would impact upon the ability of these user groups to interact with a BCI. APPROACH: Electrophysiological differences between patient groups and healthy users are measured in terms of sensorimotor rhythm deflections from baseline during MI, electroencephalogram microstate scalp maps and strengths of inter-channel phase synchronization. Additionally, using a common spatial pattern algorithm and a linear discriminant analysis classifier, the classification accuracy was calculated and compared between groups. MAIN RESULTS: It is seen that both patient groups (tetraplegic and paraplegic) have some significant differences in event-related desynchronization strengths, exhibit significant increases in synchronization and reach significantly lower accuracies (mean (M) = 66.1%) than the group of healthy subjects (M = 85.1%). SIGNIFICANCE: The results demonstrate significant differences in electrophysiological correlates of motor control between healthy individuals and those individuals who stand to benefit most from BCI technology (individuals with SCI). They highlight the difficulty in directly translating results from healthy subjects to participants with SCI and the challenges that, therefore, arise in providing BCIs to such individuals.

Neurocognitive control in dance perception and performance

Dance is a rich source of material for researchers interested in the integration of movement and cognition. The multiple aspects of embodied cognition involved in performing and perceiving dance have inspired scientists to use dance as a means for studying motor control, expertise, and action-perception links. The aim of this review is to present basic research on cognitive and neural processes implicated in the execution, expression, and observation of dance, and to bring into relief contemporary issues and open research questions. The review addresses six topics: 1) dancers’ exemplary motor control, in terms of postural control, equilibrium maintenance, and stabilization; 2) how dancers’ timing and on-line synchronization are influenced by attention demands and motor experience; 3) the critical roles played by sequence learning and memory; 4) how dancers make strategic use of visual and motor imagery; 5) the insights into the neural coupling between action and perception yielded through exploration of the brain architecture mediating dance observation; and 6) a neuroaesthetics perspective that sheds new light on the way audiences perceive and evaluate dance expression. Current and emerging issues are presented regarding future directions that will facilitate the ongoing dialogue between science and dance.

Who knows best? Awareness of divided attention difficulty in a neurological rehabilitation setting

Objective: To explore whether patients relearning to walk after acquired brain injury and showing cognitive-motor interference were aware of divided attention difficulty; whether their perceptions concurred with those of treating staff. Design: Patients and neurophysiotherapists (from rehabilitation and disabled wards) completed questionnaires. Factor analyses were applied to responses. Correlations between responses, clinical measures and experimental decrements were examined. Results: Patient/staff responses showed some agreement; staff reported higher levels of perceived difficulty; responses conformed to two factors. One factor (staff/patients alike) reflected expectations about functional/motor status and did not correlate with decrements. The other factor (patients) correlated significantly with dual-task motor decrement, suggesting some genuine awareness of difficulty (cognitive performance prioritized over motor control). The other factor (staff) correlated significantly with cognitive decrement (gait prioritized over sustained attention). Conclusions: Despite some inaccurate estimation of susceptibility; patients and staff do exhibit awareness of divided attention difficulty, but with a limited degree of concurrence. In fact, our results suggest that patients and staff may be sensitive to different aspects of the deficit. Rather than 'Who knows best?', it is a question of 'Who knows what?.

Advances in upper limb stroke rehabilitation: a technology push

Strokes affect thousands of people worldwide leaving sufferers with severe disabilities affecting their daily activities. In recent years, new rehabilitation techniques have emerged such as constraint-induced therapy, biofeedback therapy and robot-aided therapy. In particular, robotic techniques allow precise recording of movements and application of forces to the affected limb, making it a valuable tool for motor rehabilitation. In addition, robot-aided therapy can utilise visual cues conveyed on a computer screen to convert repetitive movement practice into an engaging task such as a game. Visual cues can also be used to control the information sent to the patient about exercise performance and to potentially address psychosomatic variables influencing therapy. This paper overviews the current state-of-the-art on upper limb robot-mediated therapy with a focal point on the technical requirements of robotic therapy devices leading to the development of upper limb rehabilitation techniques that facilitate reach-to-touch, fine motor control, whole-arm movements and promote rehabilitation beyond hospital stay. The reviewed literature suggest that while there is evidence supporting the use of this technology to reduce functional impairment, besides the technological push, the challenge ahead lies on provision of effective assessment of outcome and modalities that have a stronger impact transferring functional gains into functional independence.

Rapid evolution of the cerebellum in humans and other great apes

Humans’ unique cognitive abilities are usually attributed to a greatly expanded neocortex, which has been described as “the crowning achievement of evolution and the biological substrate of human mental prowess” [1]. The human cerebellum, however, contains four times more neurons than the neocortex [2] and is attracting increasing attention for its wide range of cognitive functions. Using a method for detecting evolutionary rate changes along the branches of phylogenetic trees, we show that the cerebellum underwent rapid size increase throughout the evolution of apes, including humans, expanding significantly faster than predicted by the change in neocortex size. As a result, humans and other apes deviated significantly from the general evolutionary trend for neocortex and cerebellum to change in tandem, having significantly larger cerebella relative to neocortex size than other anthropoid primates. These results suggest that cerebellar specialization was a far more important component of human brain evolution than hitherto recognized and that technical intelligence was likely to have been at least as important as social intelligence in human cognitive evolution. Given the role of the cerebellum in sensory-motor control and in learning complex action sequences, cerebellar specialization is likely to have underpinned the evolution of humans’ advanced technological capacities, which in turn may have been a preadaptation for language.

A new paradigm for BCI research

A new control paradigm for Brain Computer Interfaces (BCIs) is proposed. BCIs provide a means of communication direct from the brain to a computer that allows individuals with motor disabilities an additional channel of communication and control of their external environment. Traditional BCI control paradigms use motor imagery, frequency rhythm modification or the Event Related Potential (ERP) as a means of extracting a control signal. A new control paradigm for BCIs based on speech imagery is initially proposed. Further to this a unique system for identifying correlations between components of the EEG and target events is proposed and introduced.

Disruption of sitting balance after stroke: influence of spoken output

Objectives: To identify the extent of dual task interference between cognitive and motor tasks, (cognitive motor interference (CMI)) in sitting balance during recovery from stroke; to compare CMI in sitting balance between stroke and non-stroke groups; and to record any changes to CMI during sitting that correlate with functional recovery. Method: 36 patients from stroke rehabilitation settings in three NHS trusts. Healthy control group: 21 older volunteers. Measures of seated postural sway were taken in unsupported sitting positions, alone, or concurrently with either a repetitive utterance task or an oral word category generation task. Outcome measures were variability of sway area, path length of sway, and the number of valid words generated. Results: Stroke patients were generally less stable than controls during unsupported sitting tasks. They showed greater sway during repetitive speech compared with quiet sitting, but did not show increased instability to posture between repetitive speech and word category generation. When compared with controls, stroke patients experienced greater dual task interferences during repetitive utterance but not during word generation. Sway during repetitive speech was negatively correlated with concurrent function on the Barthel ADL index. Conclusions: The stroke patients showed postural instability and poor word generation skills. The results of this study show that the effort of verbal utterances alone was sufficient to disturb postural control early after stroke, and the extent of this instability correlated with concomitant Barthel ADL function.

Neural systems in the visual control of steering

Visual control of locomotion is essential for most mammals and requires coordination between perceptual processes and action systems. Previous research on the neural systems engaged by self-motion has focused on heading perception, which is only one perceptual subcomponent. For effective steering, it is necessary to perceive an appropriate future path and then bring about the required change to heading. Using function magnetic resonance imaging in humans, we reveal a role for the parietal eye fields (PEFs) in directing spatially selective processes relating to future path information. A parietal area close to PEFs appears to be specialized for processing the future path information itself. Furthermore, a separate parietal area responds to visual position error signals, which occur when steering adjustments are imprecise. A network of three areas, the cerebellum, the supplementary eye fields, and dorsal premotor cortex, was found to be involved in generating appropriate motor responses for steering adjustments. This may reflect the demands of integrating visual inputs with the output response for the control device.

Determining feature relevance for the grouping of motor unit action potentials through generative topographic mapping

The study of motor unit action potential (MUAP) activity from electrornyographic signals is an important stage on neurological investigations that aim to understand the state of the neuromuscular system. In this context, the identification and clustering of MUAPs that exhibit common characteristics, and the assessment of which data features are most relevant for the definition of such cluster structure are central issues. In this paper, we propose the application of an unsupervised Feature Relevance Determination (FRD) method to the analysis of experimental MUAPs obtained from healthy human subjects. In contrast to approaches that require the knowledge of a priori information from the data, this FRD method is embedded on a constrained mixture model, known as Generative Topographic Mapping, which simultaneously performs clustering and visualization of MUAPs. The experimental results of the analysis of a data set consisting of MUAPs measured from the surface of the First Dorsal Interosseous, a hand muscle, indicate that the MUAP features corresponding to the hyperpolarization period in the physisiological process of generation of muscle fibre action potentials are consistently estimated as the most relevant and, therefore, as those that should be paid preferential attention for the interpretation of the MUAP groupings.

Architecture for neuronal cell control of a mobile robot

It is usually expected that the intelligent controlling mechanism of a robot is a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot - thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. In particular, the use of rodent primary dissociated cultured neuronal networks for the control of mobile `animals' (artificial animals, a contraction of animal and materials) is a novel approach to discovering the computational capabilities of networks of biological neurones. A dissociated culture of this nature requires appropriate embodiment in some form, to enable appropriate development in a controlled environment within which appropriate stimuli may be received via sensory data but ultimate influence over motor actions retained. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animal) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This 'closed loop' interaction with the environment through both sensing and effecting will enable investigation of its learning capacity This paper details the components of the overall animat closed loop system and reports on the evaluation of the results from the experiments being carried out with regard to robot behaviour.

Impedance mismatch: some differences between the way humans and robots control interaction forces

In over forty years of research robots have made very little progress still largely confined to industrial manufacture and cute toys, yet in the same period computing has followed Moores Law where the capacity double roughly every two years. So why is there no Moores Law for robots? Two areas stand out as worthy of research to speedup progress. The first is to get a greater understanding of how human and animal brains control movement, the second to build a new generation of robots that have greater haptic sense, that is a better ability to adapt to the environment as it is encountered. A remarkable property of the cognitive-motor system in humans and animals is that it is slow. Recognising an object may take 250 mS, a reaction time of 150 mS is considered fast. Yet despite this slow system we are well designed to allow contact with the world in a variety of ways. We can anticipate an encounter, use the change of force as a means of communication and ignore sensory cues when they are not relevant. A better understanding of these process has allowed us to build haptic interfaces to mimic the interaction. Emerging from this understanding are new ways to control the contact between robots, the user and the environment. Rehabilitation robotics has all the elements in the subject to not only enable and change the lives of people with disabilities, but also to facilitate revolution change in classic robotics.

Generative topographic mapping applied to clustering and visualization of motor unit action potentials

The identification and visualization of clusters formed by motor unit action potentials (MUAPs) is an essential step in investigations seeking to explain the control of the neuromuscular system. This work introduces the generative topographic mapping (GTM), a novel machine learning tool, for clustering of MUAPs, and also it extends the GTM technique to provide a way of visualizing MUAPs. The performance of GTM was compared to that of three other clustering methods: the self-organizing map (SOM), a Gaussian mixture model (GMM), and the neural-gas network (NGN). The results, based on the study of experimental MUAPs, showed that the rate of success of both GTM and SOM outperformed that of GMM and NGN, and also that GTM may in practice be used as a principled alternative to the SOM in the study of MUAPs. A visualization tool, which we called GTM grid, was devised for visualization of MUAPs lying in a high-dimensional space. The visualization provided by the GTM grid was compared to that obtained from principal component analysis (PCA). (c) 2005 Elsevier Ireland Ltd. All rights reserved.

Thought communication and control: a first step using radiotelegraphy

A signalling procedure is described involving a connection, via the Internet, between the nervous system of an able-bodied individual and a robotic prosthesis, and between the nervous systems of two able-bodied human subjects. Neural implant technology is used to directly interface each nervous system with a computer. Neural motor unit and sensory receptor recordings are processed real-time and used as the communication basis. This is seen as a first step towards thought communication, in which the neural implants would be positioned in the central nervous systems of two individuals.