3,693 research outputs found

    Information decomposition of multichannel EMG to map functional interactions in the distributed motor system

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    The central nervous system needs to coordinate multiple muscles during postural control. Functional coordination is established through the neural circuitry that interconnects different muscles. Here we used multivariate information decomposition of multichannel EMG acquired from 14 healthy participants during postural tasks to investigate the neural interactions between muscles. A set of information measures were estimated from an instantaneous linear regression model and a time-lagged VAR model fitted to the EMG envelopes of 36 muscles. We used network analysis to quantify the structure of functional interactions between muscles and compared them across experimental conditions. Conditional mutual information and transfer entropy revealed sparse networks dominated by local connections between muscles. We observed significant changes in muscle networks across postural tasks localized to the muscles involved in performing those tasks. Information decomposition revealed distinct patterns in task-related changes: unimanual and bimanual pointing were associated with reduced transfer to the pectoralis major muscles, but an increase in total information compared to no pointing, while postural instability resulted in increased information, information transfer and information storage in the abductor longus muscles compared to normal stability. These findings show robust patterns of directed interactions between muscles that are task-dependent and can be assessed from surface EMG recorded during static postural tasks. We discuss directed muscle networks in terms of the neural circuitry involved in generating muscle activity and suggest that task-related effects may reflect gain modulations of spinal reflex pathways

    Temporal adaptation and anticipation mechanisms in sensorimotor synchronization

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    Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger movement

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    The capacity of the human brain to interpret and respond to multiple temporal scales in its surroundings suggests that its internal interactions must also be able to operate over a broad temporal range. In this paper, we utilize a recently introduced method for characterizing the rate of change of the phase difference between MEG signals and use it to study the temporal structure of the phase interactions between MEG recordings from the left and right motor cortices during rest and during a finger-tapping task. We use the Hilbert transform to estimate moment-to-moment fluctuations of the phase difference between signals. After confirming the presence of scale-invariance we estimate the Hurst exponent using detrended fluctuation analysis (DFA). An exponent of >0.5 is indicative of long-range temporal correlations (LRTCs) in the signal. We find that LRTCs are present in the α/μ and β frequency bands of resting state MEG data. We demonstrate that finger movement disrupts LRTCs correlations, producing a phase relationship with a structure similar to that of Gaussian white noise. The results are validated by applying the same analysis to data with Gaussian white noise phase difference, recordings from an empty scanner and phase-shuffled time series. We interpret the findings through comparison of the results with those we obtained from an earlier study during which we adopted this method to characterize phase relationships within a Kuramoto model of oscillators in its sub-critical, critical, and super-critical synchronization states. We find that the resting state MEG from left and right motor cortices shows moment-to-moment fluctuations of phase difference with a similar temporal structure to that of a system of Kuramoto oscillators just prior to its critical level of coupling, and that finger tapping moves the system away from this pre-critical state toward a more random state

    Evolutionary robotics and neuroscience

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    Analysis of sensorimotor rhythms based on lower-limbs motor imagery for brain-computer interface

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    Over recent years significant advancements in the field of assistive technologies have been observed. One of the paramount needs for the development and advancement that urged researchers to contribute in the field other than congenital or diagnosed chronic disorders, is the rising number of affectees from accidents, natural calamity (due to climate change), or warfare, worldwide resulting in spinal cord injuries (SCI), neural disorder, or amputation (interception) of limbs, that impede a human to live a normal life. In addition to this, more than ten million people in the world are living with some form of handicap due to the central nervous system (CNS) disorder, which is precarious. Biomedical devices for rehabilitation are the center of research focus for many years. For people with lost motor control, or amputation, but unscathed sensory control, instigation of control signals from the source, i.e. electrophysiological signals, is vital for seamless control of assistive biomedical devices. Control signals, i.e. motion intentions, arouse    in the sensorimotor cortex of the brain that can be detected using invasive or non-invasive modality. With non-invasive modality, the electroencephalography (EEG) is used to record these motion intentions encoded in electrical activity of the cortex, and are deciphered to recognize user intent for locomotion. They are further transferred to the actuator, or end effector of the assistive device for control purposes. This can be executed via the brain-computer interface (BCI) technology. BCI is an emerging research field that establishes a real-time bidirectional connection between the human brain and a computer/output device. Amongst its diverse applications, neurorehabilitation to deliver sensory feedback and brain controlled biomedical devices for rehabilitation are most popular. While substantial literature on control of upper-limb assistive technologies controlled via BCI is there, less is known about the lower-limb (LL) control of biomedical devices for navigation or gait assistance via BCI. The types  of EEG signals compatible with an independent BCI are the oscillatory/sensorimotor rhythms (SMR) and event-related potential (ERP). These signals have successfully been used in BCIs for navigation control of assistive devices. However, ERP paradigm accounts for a voluminous setup for stimulus presentation to the user during operation of BCI assistive device. Contrary to this, the SMR does not require large setup for activation of cortical activity; it instead depends on the motor imagery (MI) that is produced synchronously or asynchronously by the user. MI is a covert cognitive process also termed kinaesthetic motor imagery (KMI) and elicits clearly after rigorous training trials, in form of event-related desynchronization (ERD) or synchronization (ERS), depending on imagery activity or resting period. It usually comprises of limb movement tasks, but is not limited to it in a BCI paradigm. In order to produce detectable features that correlate to the user¿s intent, selection of cognitive task is an important aspect to improve the performance of a BCI. MI used in BCI predominantly remains associated with the upper- limbs, particularly hands, due to the somatotopic organization of the motor cortex. The hand representation area is substantially large, in contrast to the anatomical location of the LL representation areas in the human sensorimotor cortex. The LL area is located within the interhemispheric fissure, i.e. between the mesial walls of both hemispheres of the cortex. This makes it arduous to detect EEG features prompted upon imagination of LL. Detailed investigation of the ERD/ERS in the mu and beta oscillatory rhythms during left and right LL KMI tasks is required, as the user¿s intent to walk is of paramount importance associated to everyday activity. This is an important area of research, followed by the improvisation of the already existing rehabilitation system that serves the LL affectees. Though challenging, solution to these issues is also imperative for the development of robust controllers that follow the asynchronous BCI paradigms to operate LL assistive devices seamlessly. This thesis focusses on the investigation of cortical lateralization of ERD/ERS in the SMR, based on foot dorsiflexion KMI and knee extension KMI separately. This research infers the possibility to deploy these features in real-time BCI by finding maximum possible classification accuracy from the machine learning (ML) models. EEG signal is non-stationary, as it is characterized by individual-to-individual and trial-to-trial variability, and a low signal-to-noise ratio (SNR), which is challenging. They are high in dimension with relatively low number of samples available for fitting ML models to the data. These factors account for ML methods that were developed into the tool of choice  to analyse single-trial EEG data. Hence, the selection of appropriate ML model for true detection of class label with no tradeoff of overfitting is crucial. The feature extraction part of the thesis constituted of testing the band-power (BP) and the common spatial pattern (CSP) methods individually. The study focused on the synchronous BCI paradigm. This was to ensure the exhibition of SMR for the possibility of a practically viable control system in a BCI. For the left vs. right foot KMI, the objective was to distinguish the bilateral tasks, in order to use them as unilateral commands in a 2-class BCI for controlling/navigating a robotic/prosthetic LL for rehabilitation. Similar was the approach for left-right knee KMI. The research was based on four main experimental studies. In addition to the four studies, the research is also inclusive of the comparison of intra-cognitive tasks within the same limb, i.e. left foot vs. left knee and right foot vs. right knee tasks, respectively (Chapter 4). This added to another novel contribution towards the findings based on comparison of different tasks within the same LL. It provides basis to increase the dimensionality of control signals within one BCI paradigm, such as a BCI-controlled LL assistive device with multiple degrees of freedom (DOF) for restoration of locomotion function. This study was based on analysis of statistically significant mu ERD feature using BP feature extraction method. The first stage of this research comprised of the left vs. right foot KMI tasks, wherein the ERD/ERS that elicited in the mu-beta rhythms were analysed using BP feature extraction method (Chapter 5). Three individual features, i.e. mu ERD, beta ERD, and beta ERS were investigated on EEG topography and time-frequency (TF) maps, and average time course of power percentage, using the common average reference and bipolar reference methods. A comparative study was drawn for both references to infer the optimal method. This was followed by ML, i.e. classification of the three feature vectors (mu ERD, beta ERD, and beta ERS), using linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbour (KNN) algorithms, separately. Finally, the multiple correction statistical tests were done, in order to predict maximum possible classification accuracy amongst all paradigms for the most significant feature. All classifier models were supported with the statistical techniques of k-fold cross validation and evaluation of area under receiver-operator characteristic curves (AUC-ROC) for prediction of the true class label. The highest classification accuracy of 83.4% ± 6.72 was obtained with KNN model for beta ERS feature. The next study was based on enhancing the classification accuracy obtained from previous study. It was based on using similar cognitive tasks as study in Chapter 5, however deploying different methodology for feature extraction and classification procedure. In the second study, ERD/ERS from mu and beta rhythms were extracted using CSP and filter bank common spatial pattern (FBCSP) algorithms, to optimize the individual spatial patterns (Chapter 6). This was followed by ML process, for which the supervised logistic regression (Logreg) and LDA were deployed separately. Maximum classification accuracy resulted in 77.5% ± 4.23 with FBCSP feature vector and LDA model, with a maximum kappa coefficient of 0.55 that is in the moderate range of agreement between the two classes. The left vs. right foot discrimination results were nearly same, however the BP feature vector performed better than CSP. The third stage was based on the deployment of novel cognitive task of left vs. right knee extension KMI. Analysis of the ERD/ERS in the mu-beta rhythms was done for verification of cortical lateralization via BP feature vector (Chapter 7). Similar to Chapter 5, in this study the analysis of ERD/ERS features was done on the EEG topography and TF maps, followed by the determination of average time course and peak latency of feature occurrence. However, for this study, only mu ERD and beta ERS features were taken into consideration and the EEG recording method only comprised of common average reference. This was due to the established results from the foot study earlier, in Chapter 5, where beta ERD features showed less average amplitude. The LDA and KNN classification algorithms were employed. Unexpectedly, the left vs. right knee KMI reflected the highest accuracy of 81.04% ± 7.5 and an AUC-ROC = 0.84, strong enough to be used in a real-time BCI as two independent control features. This was using KNN model for beta ERS feature. The final study of this research followed the same paradigm as used in Chapter 6, but for left vs. right knee KMI cognitive task (Chapter 8). Primarily this study aimed at enhancing the resulting accuracy from Chapter 7, using CSP and FBCSP methods with Logreg and LDA models respectively. Results were in accordance with those of the already established foot KMI study, i.e. BP feature vector performed better than the CSP. Highest classification accuracy of 70.00% ± 2.85 with kappa score of 0.40 was obtained with Logreg using FBCSP feature vector. Results stipulated the utilization of ERD/ERS in mu and beta bands, as independent control features for discrimination of bilateral foot or the novel bilateral knee KMI tasks. Resulting classification accuracies implicate that any 2-class BCI, employing unilateral foot, or knee KMI, is suitable for real-time implementation. In conclusion, this thesis demonstrates the possible EEG pre-processing, feature extraction and classification methods to instigate a real-time BCI from the conducted studies. Following this, the critical aspects of latency in information transfer rate, SNR, and tradeoff between dimensionality and overfitting needs to be taken care of, during design of real-time BCI controller. It also highlights that there is a need for consensus over the development of standardized methods of cognitive tasks for MI based BCI. Finally, the application of wireless EEG for portable assistance is essential as it will contribute to lay the foundations of the development of independent asynchronous BCI based on SMR

    Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

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    Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain–body– environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour

    Adaptation to temporal structure

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    Adaptation to temporal structure

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