A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Corticospinal excitability not affected by negative motor imagery or intention

Bien que l’imagerie motrice positive ait été bien étudiée et est utilisée en réhabilitation, l’effet de l’imagerie motrice négative est beaucoup moins connu. Le but de cette recherche était de définir si l’intention et/ou l’imagerie motrice négative serait en mesure de réduire l’effet d’une stimulation magnétique transcrânienne (SMT) sur le cortex moteur. Vingt participants ont reçu trente stimulations de SMT dans trois situations différentes : En restant passif, en portant une attention particulière aux sensations dans leur main ou en tentant de réduire l’effet de la SMT. La moitié des participants ont utilisé une stratégie d’imagerie motrice, l’autre moitié leur intention. Dans les deux cas, l’amplitude dans la condition de modulation n’a pas été réduite de façon significative.

The Role of Kinaesthetic Motor Imagery in Promoting Activation of the Lumbrical Muscles in the Hemiparetic Hand: a Randomised Controlled Study

The objective of the investigation who gave as result this work was to investigate the effectiveness of kinaesthetic motor imagery in the activation of the hemiplegic hand muscles following stroke. The experiment consisted of two random groups. Movements were measured after treatment. The participants were ten patients with hemiplegic hands (men who mean age was 74.4 years; mean time since stroke 3.05 months). All patients received three sessions of physical treatment based on an identical treatment protocol. Five patients were randomly assigned to an experimental group practising kinaesthetic motor imagery of a grasp using the 'lumbrical action' (experimental group). The others five (control group) followed a relaxation script. All the patients were then asked to grasp an object using the 'lumbrical action'. The grasps were recorded using an optoelectronic motion capture system. The magnitude of the extension of the index finger and the correlation of the angular displacement of the proximal phalangeal joints and the metacarpophalangeal joints were calculated. The movement time for the whole grip was calculated. The experimental group demonstrated higher extension in the index finger (p = < 0.01) and they had a higher correlation coefficient (0.99) than the control group (0.77) for the displacement of the proximal interphalangeal joint and the metacarpophalangeal joints. The movement time for the experimental group was faster, although the difference was not significant.

Neural activation and functional connectivity during motor imagery of bimanual everyday actions

Bimanual actions impose intermanual coordination demands not present during unimanual actions. We investigated the functional neuroanatomical correlates of these coordination demands in motor imagery (MI) of everyday actions using functional magnetic resonance imaging (fMRI). For this, 17 participants imagined unimanual actions with the left and right hand as well as bimanual actions while undergoing fMRI. A univariate fMRI analysis showed no reliable cortical activations specific to bimanual MI, indicating that intermanual coordination demands in MI are not associated with increased neural processing. A functional connectivity analysis based on psychophysiological interactions (PPI), however, revealed marked increases in connectivity between parietal and premotor areas within and between hemispheres. We conclude that in MI of everyday actions intermanual coordination demands are primarily met by changes in connectivity between areas and only moderately, if at all, by changes in the amount of neural activity. These results are the first characterization of the neuroanatomical correlates of bimanual coordination demands in MI. Our findings support the assumed equivalence of overt and imagined actions and highlight the differences between uni- and bimanual actions. The findings extent our understanding of the motor system and may aid the development of clinical neurorehabilitation approaches based on mental practice.

Effect of instructive visual stimuli on neurofeedback training for motor imagery-based braincomputer interface

Event-related desynchronization (ERD) of the electroencephalogram (EEG) from the motor cortex is associated with execution, observation, and mental imagery of motor tasks. Generation of ERD by motor imagery (MI) has been widely used for brain-computer interfaces (BCIs) linked to neuroprosthetics and other motor assistance devices. Control of MI-based BCIs can be acquired by neurofeedback training to reliably induce MI-associated ERD. To develop more effective training conditions, we investigated the effect of static and dynamic visual representations of target movements (a picture of forearms or a video clip of hand grasping movements) during the BCI training. After 4 consecutive training days, the group that performed MI while viewing the video showed significant improvement in generating MI-associated ERD compared with the group that viewed the static image. This result suggests that passively observing the target movement during MI would improve the associated mental imagery and enhance MI-based BCIs skills.

Analysis of functional networks involved in motor execution and motor imagery using combined hierarchical clustering analysis and independent component analysis

Cognitive experiments involving motor execution (ME) and motor imagery (MI) have been intensively studied using functional magnetic resonance imaging (fMRI). However, the functional networks of a multitask paradigm which include ME and MI were not widely explored. In this article, we aimed to investigate the functional networks involved in MI and ME using a method combining the hierarchical clustering analysis (HCA) and the independent component analysis (ICA). Ten right-handed subjects were recruited to participate a multitask experiment with conditions such as visual cue, MI, ME and rest. The results showed that four activation clusters were found including parts of the visual network, ME network, the MI network and parts of the resting state network. Furthermore, the integration among these functional networks was also revealed. The findings further demonstrated that the combined HCA with ICA approach was an effective method to analyze the fMRI data of multitasks.

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.

Corticospinal excitability following short-term motor imagery training of a strength task

Motor imagery and actual movement engage similar neural structures, however, whether they produce similar training-related corticospinal adaptations has yet to be established. The aim of this study was to compare changes in strength and corticospinal excitability following short-term motor imagery strength training and short-term strength training. Transcranial magnetic stimulation (TMS) was applied over the contralateral motor cortex (M1) to elicit motor-evoked potentials in the dominant biceps brachii muscle prior to and following 3-week strength training using actual bicep curls or motor imagery of bicep curls. The strength training (n = 6) and motor imagery (n = 6) groups underwent three supervised training sessions per week for 3 weeks. Participants completed four sets of six to eight repetitions (actual or imagined) at a training load of 80% of their one-repetition maximum. The control group (n = 6) were required to maintain their current level of physical activity. Both training groups exhibited large performance gains in strength (p < 0.001; strength training 39% improvement, imagery 16% improvement), which were significantly different between groups (p = 0.027). TMS revealed that the performance improvements observed in both imagery and strength training were accompanied by increases in corticospinal excitability (p < 0.001), however, these differences were not significantly different between groups (p = 0.920). Our findings suggest that both strength training and motor imagery training utilised similar neural substrates within the primary M1, however, strength training resulted in greater gains in strength than motor imagery strength training. This difference in strength increases may be attributed to adaptations during strength training that are not confined to the primary M1. These findings have theoretical implications for functional equivalent views of motor imagery as well as important therapeutic implications.

Does implicit motor imagery ability predict reaching correction efficiency? A test of recent models of human motor control

Neurocomputational models of reaching indicate that efficient purposive correction of movement midflight (e.g., online control) depends on one's ability to generate and monitor an accurate internal (neural) movement representation. In the first study to test this empirically, the authors investigated the relationship between healthy young adults’ implicit motor imagery performance and their capacity to correct their reaching trajectory. As expected, after controlling for general reaching speed, hierarchical regression demonstrated that imagery ability was a significant predictor of hand correction speed; that is, faster and more accurate imagery performance associated with faster corrections to reaching following target displacement at movement onset. They argue that these findings provide preliminary support for the view that a link exists between an individual's ability to represent movement mentally and correct movement online efficiently.