Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study

Background and Purpose: Impaired hand function decreases quality of life in persons with tetraplegia. We tested functional electrical stimulation (FES) controlled by a hybrid brain-computer interface (BCI) for improving hand function in participants with tetraplegia. Methods: Two participants with subacute tetraplegia (participant 1: C5 Brown-Sequard syndrome, participant 2: complete C5 lesion) took part in this proof-of-concept study. The goal was to determine whether the BCI system could drive the FES device by accurately classifying participants' intent (open or close the hand). Participants 1 and 2 received 10 sessions and 4 sessions of BCI-FES, respectively. A novel time-switch BCI strategy based on motor imagery was used to activate the FES. In one session, we tested a hybrid BCI-FES based on 2 spontaneously generated brain rhythms: a sensory-motor rhythm during motor imagery to activate a stimulator and occipital alpha rhythms to deactivate the stimulator. Participants received BCI-FES therapy 2 to 3 times a week in addition to conventional therapy. Imagery ability and muscle strength were measured before and after treatment. Results: Visual feedback was associated with a 4-fold increase of brain response during motor imagery in both participants. For participant 1, classification accuracy (open/closed) for motor imagery-based BCI was 83.5% (left hand) and 83.8% (right hand); participant 2 had a classification accuracy of 83.8% for the right hand. Participant 1 had moderate improvement in muscle strength, while there was no change for participant 2. Discussion and Conclusion: We demonstrated feasibility of BCI-FES, using 2 naturally generated brain rhythms. Studies on a larger number of participants are needed to separate the effects of BCI training from effects of conventional therapy

Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG

This study investigates the indications of non-linear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven non-linear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, non-linear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of non-linearity in the EEG signals. However the rejection of the null hypothesis of non-linearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying non-linear dynamics in the signals. Across non-linear test statistics, the highest degree of non-linearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings

Temporal dynamics of travelling theta wave activity in infants responding to visual looming

A fundamental property of most animals is the ability to see whether an object is approaching on a direct collision course and, if so, when it will collide. Using high-density electroencephalography in 5- to 11-month-old infants and a looming stimulus approaching under three different accelerations, we investigated how the young human nervous system extracts and processes information for impending collision. Here we show that infants' looming related brain activity is characterized by theta oscillations. Source analyses reveal clear localised activity in the visual cortex. Analysing the temporal dynamics of the source waveform, we provide evidence that the temporal structure of different looming stimuli is sustained during processing in the more mature infant brain, providing infants with increasingly veridical time-to-collision information about looming danger as they grow older and become mobile

The impact of temporal synchronisation imprecision on TRF analyses

Human sensory perception requires our brains to extract, encode, and process multiple properties of the sensory input. In the context of continuous sensory signals, such as speech and music, the measured electrical neural activity synchronises to properties such as the acoustic envelope, a phenomenon referred to as neural tracking. The ability of measuring neural tracking with non-invasive neurophysiology constitutes an exciting new opportunity for applied research. For example, it enables the objective assessment of cognitive functions in challenging cohorts and environments by using pleasant, everyday tasks, such as watching videos. However, neural tracking has been mostly studied in controlled, laboratory environments guaranteeing precise synchronisation between the neural signal and the corresponding labels (e.g., speech envelope). There exist various challenges that could impact such a temporal precision in, for instance, out-of-lab scenarios, such as technology (e.g., wireless data acquisition), mobility requirements (e.g., clinical scenarios), and the task (e.g., imagery). Aiming to address this type of challenge, we focus on the predominant scenario of continuous sensory experiments involving listening to speech and music. First a temporal response function analysis is presented on two different datasets to assess the impact of trigger imprecision. Second, a proof-of-concept re-alignment methodology is proposed to determine potential issues with the temporal synchronisation. Finally, a use-case study is presented that demonstrates neural tracking measurements in a challenging scenario involving older individuals with neurocognitive decline in care homes. Significance Statement Human cognitive functions can be studied by measuring neural tracking with non-invasive neurophysiology as participants perform pleasant, everyday tasks, such as listening to music. However, while recent work has encouraged the use of this approach in applied research, it remains unclear how robust neural tracking measurements can be when considering the methodological constraints of applied scenarios. This study determines the impact of a key factor for the measurement of neural tracking: the temporal precision of the neural recording. The results provide clear guidelines for future research, indicating what level of imprecision can be tolerated for measuring neural tracking with speech and music listening tasks in both laboratory and applied settings. Furthermore, the study provides a strategy to assess the impact of imprecision in the synchronisation of the neural recording, thus developing new tools for applied neuroscience

Pathological slow-wave activity and impaired working memory binding in post-traumatic amnesia

Associative binding is key to normal memory function and is transiently disrupted during periods of post-traumatic amnesia (PTA) following traumatic brain injury (TBI). Electrophysiological abnormalities including low-frequency activity are common following TBI. Here, we investigate associative memory binding during PTA and test the hypothesis that misbinding is caused by pathological slowing of brain activity disrupting cortical communication. Thirty acute moderate-severe TBI patients (25 males; 5 females) and 26 healthy controls (20 males; 6 females) were tested with a precision working memory paradigm requiring the association of object and location information. Electrophysiological effects of TBI were assessed using resting-state EEG in a subsample of 17 patients and 21 controls. PTA patients showed abnormalities in working memory function and made significantly more misbinding errors than patients who were not in PTA and controls. The distribution of localisation responses was abnormally biased by the locations of non-target items for patients in PTA suggesting a specific impairment of object and location binding. Slow wave activity was increased following TBI. Increases in the delta-alpha ratio indicative of an increase in low-frequency power specifically correlated with binding impairment in working memory. Connectivity changes in TBI did not correlate with binding impairment. Working memory and electrophysiological abnormalities normalised at six-month follow-up. These results show that patients in PTA show high rates of misbinding that are associated with a pathological shift towards lower frequency oscillations

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity

Auditory Development between 7 and 11 Years: An Event-Related Potential (ERP) Study

Background: There is considerable uncertainty about the time-course of central auditory maturation. On some indices, children appear to have adult-like competence by school age, whereas for other measures development follows a protracted course. Methodology: We studied auditory development using auditory event-related potentials (ERPs) elicited by tones in 105 children on two occasions two years apart. Just over half of the children were 7 years initially and 9 years at follow-up, whereas the remainder were 9 years initially and 11 years at follow-up. We used conventional analysis of peaks in the auditory ERP, independent component analysis, and time-frequency analysis. Principal Findings: We demonstrated maturational changes in the auditory ERP between 7 and 11 years, both using conventional peak measurements, and time-frequency analysis. The developmental trajectory was different for temporal vs. fronto-central electrode sites. Temporal electrode sites showed strong lateralisation of responses and no increase of low-frequency phase-resetting with age, whereas responses recorded from fronto-central electrode sites were not lateralised and showed progressive change with age. Fronto-central vs. temporal electrode sites also mapped onto independent components with differently oriented dipole sources in auditory cortex. A global measure of waveform shape proved to be the most effective method for distinguishing age bands. Conclusions/Significance: The results supported the idea that different cortical regions mature at different rates. The ICC measure is proposed as the best measure of 'auditory ERP age'

Closed-loop photic stimulation using EEG

Accumulating evidence indicates that neural oscillations play a central role in brain function, organising and modulating brain activity in response to both bottom-up exogenous stimuli and top-down endogenous dynamics. It has been pointed out that abnormalities in oscillatory dynamics are sometimes associated with various neuropathologies. Consequently, it has been posited that normalising pathological oscillations in these conditions may reduce negative symptoms of brain disorders. One potential technique for this is photic brain stimulation. However, while it has been shown to be able to increase the power of targeted frequency bands during stimulation, it remains an open question as to whether it is possible to also suppress the amplitude of a frequency band by rhythmic light stimulation. In this thesis, we will explore different light stimulation protocols, assessing whether a more general control of the alpha frequency band is possible. Specifically, we are exploring the during- and post-stimulation impact of photic stimulation and the feasibility that closed-loop interaction between high-luminance LEDs and an electroencephalography (EEG) brain activity signal can both increase and decrease alpha frequency power across the visual cortex while stimulation is being applied