EEG correlates and methods for learning in brain-computer interaction.

Motor Imagery (MI)-based Brain-Computer Interface (BCI) has emerged as a promising approach to provide an alternative means of communication, control and rehabilitation for people with severe motor impairments. However, the efficiency and efficacy of BCI systems remain to date rather limited, preventing their out-of-lab implementation. This thesis offers a few stepping stones towards more user-oriented BCI, shifting the focus to subject learning, neuroplasticity monitoring and the co-adaptation between the human and the ML BCI decoder. First, I seek to identify the electroencephalography (EEG) correlates of learning to drive a racing car, an example of complex motor skills. Additionally, I explore the role of anodal transcranial Direct Current Stimulation (tDCS) in enhancing race-driving training. My work determines that theta EEG rhythms and alpha-band effective functional connectivity between frontocentral and occipital cortical areas are salient neuromarkers of the acquisition of racing skills. I also discern a possible tDCS effect in accelerating the pace of learning. My thesis presents a novel feature selection method which combines the conventional data-driven approach with BCI expert knowledge through Fuzzy Logic. I show that my algorithm achieves statistically significant improvement in terms of classification accuracy, feature stability and class bias. The proposed method can promote subject learning during BCI training by keeping the selected features within a “learnable”, physiologically relevant manifold. One of the main motivations behind co-adaptative BCI has been the avoidance of boring and laborious open-loop calibration sessions, imposed at the beginning of user training to collect data for ML BCI model training. For BCI-based rehabilitation, these issues become pressing, demotivating for the patients and hard to fit logistically into a strict clinical schedule. Towards alleviating this issue, this thesis identifies different methods for calibration-free BCI-based rehabilitation. My results indicate that calibration-less BCI-based rehabilitation algorithms are possible without compromising performance. The proposed methods thus lift a major barrier currently obstructing the translation of BCI-based therapies

Modulation of Network Oscillations by Brain Stimulation

Finding new and effective treatments for mental illness represents one of the largest challenges of our time due the large number of people affected. Despite long and careful study there have been few recent breakthroughs in pharmacological treatments of mental illness. To address this, the National Institute of Mental Health (NIMH) has recently begun to focus on the investigation of network level correlates of mental illnesses. Patients with mental illness often exhibit aberrant neural oscillatory activity, thus making the network level a promising scale for the identification of measureable neural correlates of mental illnesses. At the network level, neural activity is primarily in the form of cortical oscillations which may be recorded noninvasively with electroencephalography (EEG). Such EEG oscillations are the result of synchronized activity from many cells in the neocortex. However the exact mechanisms of how oscillations arise and spread throughout the brain remain unknown. Non-invasive brain stimulation is a promising treatment modality because it specifically targets activity of brain networks. Unlike pharmacological treatments, stimulation with electric and magnetic fields directly targets electrical activity of many cells in a network. In particular, transcranial alternating current stimulation appears to be especially suited for targeting oscillations in brain networks. Despite the promise of these brain stimulation techniques, the underlying mechanisms remain unknown. The studies presented in this dissertation address two critical gaps in the treatment of mental illnesses. (1) How does rhythmic network activity arise from cellular and synaptic components? And (2) how does brain stimulation interact with ongoing network activity? Only by understanding how network activity arises and how it interacts with brain stimulation we may begin to design brain stimulation paradigms for treatment of mental illness.Doctor of Philosoph

Transcranial Direct Current Stimulation Modulates Working Memory Maintenance Processes in Healthy Individuals

The effects of transcranial direct current stimulation (tDCS) at the pFC are often investigated using cognitive paradigms, particularly working memory tasks. However, the neural basis for the neuromodulatory cognitive effects of tDCS, including whi ch subprocesses are af f ected by sti mul ati on, i s not completely understood. We investigated the effects of tDCS on working memory task-related spectral activity during and after tDCS to gain better insights into the neurophysiological changes associated with stimulation. We reanalyzed data from 100 healthy participants grouped by allocation to receive either sham (0 mA, 0.016 mA, and 0.034 mA) or active (1 mA or 2 mA) stimulation during a 3-back task. EEG data were used to analyze event-related spectral power in frequency bands associated with working memory performance. Frontal theta event-related synchronization (ERS) was significantly reduced post-tDCS in the active group. Participants receiving active tDCS had slower RTs following tDCS compared with sham, suggesting interference with practice effects associated with task repetition. Theta ERS was not significantly correlated with RTs or accuracy. tDCS reduced frontal theta ERS poststimulation, suggesting a selective disruption to working memory cognitive control and maintenance processes. These findings suggest that tDCS selectively affects specific subprocesses during working memory, which may explain heterogenous behavioral effects

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination\u27s complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go

Central nervous system physiology

This is the second chapter of the series on the use of clinical neurophysiology for the study of movement disorders. It focusses on methods that can be used to probe neural circuits in brain and spinal cord. These include use of spinal and supraspinal reflexes to probe the integrity of transmission in specific pathways; transcranial methods of brain stimulation such as transcranial magnetic stimulation and transcranial direct current stimulation, which activate or modulate (respectively) the activity of populations of central neurones; EEG methods, both in conjunction with brain stimulation or with behavioural measures that record the activity of populations of central neurones; and pure behavioural measures that allow us to build conceptual models of motor control. The methods are discussed mainly in relation to work on healthy individuals. Later chapters will focus specifically on changes caused by pathology

Probing fMRI brain connectivity and activity changes during emotion regulation by EEG neurofeedback

Despite the existence of several emotion regulation studies using neurofeedback, interactions among a small number of regions were evaluated, and therefore, further investigation is needed to understand the interactions of the brain regions involved in emotion regulation. We implemented electroencephalography (EEG) neurofeedback with simultaneous functional magnetic resonance imaging (fMRI) using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. Then, an explorative analysis of whole brain regions was done to understand the effect of neurofeedback on brain activity and the interaction of whole brain regions involved in emotion regulation. The participants in the control and experimental groups were asked to do emotion regulation while viewing positive images of autobiographical memories and getting sham or real (based on alpha asymmetry) EEG neurofeedback, respectively. The proposed multimodal approach quantified the effects of EEG neurofeedback in changing EEG alpha power, fMRI blood oxygenation level-dependent (BOLD) activity of prefrontal, occipital, parietal, and limbic regions (up to 1.9% increase), and functional connectivity in/between prefrontal, parietal, limbic system, and insula in the experimental group. New connectivity links were identified by comparing the brain functional connectivity between experimental conditions (Upregulation and View blocks) and also by comparing the brain connectivity of the experimental and control groups. Psychometric assessments confirmed significant changes in positive and negative mood states in the experimental group by neurofeedback. Based on the exploratory analysis of activity and connectivity among all brain regions involved in emotion regions, we found significant BOLD and functional connectivity increases due to EEG neurofeedback in the experimental group, but no learning effect was observed in the control group. The results reveal several new connections among brain regions as a result of EEG neurofeedback which can be justified according to emotion regulation models and the role of those regions in emotion regulation and recalling positive autobiographical memories

Assessing neuromodulatory effects of non-invasive brain stimulation to the prefrontal cortex

Transcranial direct current stimulation (tDCS) and theta-burst stimulation (TBS) are two non-invasive brain stimulation (NIBS) techniques that use electricity to modulate cortical activity, showing promise for the treatment of neuropsychiatric disorders like depression. However, presenting high heterogeneity in efficacy and modest effect sizes. NIBS neurophysiological effects have been usually assessed in the motor cortex, but measurements are often associated with high variability and low reliability. Because in depression, NIBS are often administered to the dorsolateral prefrontal cortex (DLPFC), it is urgent to explore cortical properties in non-motor regions. Electroencephalography (EEG) combined with TMS has permitted the investigation of brain function beyond the motor region. TMS-evoked potentials (TEPs) may provide insights into the effects and mechanisms of NIBS applied to the DLPFC. However, the sensitivity and reliability of TEPs to track excitability changes induced by NIBS on the DLPFC has not been fully elucidated. The overall aims of the thesis were to clarify these gaps and aid in the development of tDCS/TBS as clinical interventions and TMS-EEG as a tool to examine brain properties. This was addressed via an individual patient data meta-analysis (IPD-MA) examining tDCS efficacy in depression and two experimental studies in healthy evaluating TBS effects on the DLPFC using TMS-EEG and the test-retest reliability of TEPs. Study 1 was an IPD-MA evaluating tDCS antidepressant effects and predictors of response. Results showed that tDCS was moderately effective with no significant predictors identified. These findings underscored the limitations of symptom-based studies and the need to use a physiological approach (TMS-EEG) to estimate the modulatory effects of NIBS at the cortical level to improve understanding of its mechanisms and causes of the limited efficacy. Study 2 was a sham-controlled experiment in healthy participants to assess the effects of TBS on the DLPFC using TEPs. We showed that TBS could exert changes in the DLPFC responsivity, although with smaller effect sizes than prior studies. In study 3, we examined the test-retest reliability of TEPs and the modulatory effects of TBS on the DLPFC. Results showed that TEPs were reliable within-block, but only later components (N100 and P200) had good concordance between sessions, and that reliability of TBS effects in neural excitability was poor. These findings contribute to understanding NIBS effects in the DLPFC and developing TMS-EEG as a technique to assess cortical properties