Similarities between explicit and implicit motor imagery in mental rotation of hands: an EEG study

Chronometric and imaging studies have shown that motor imagery is used implicitly during mental rotation tasks in which subjects for example judge the laterality of human hand pictures at various orientations. Since explicit motor imagery is known to activate the sensorimotor areas of the cortex, mental rotation is expected to do similar if it involves a form of motor imagery. So far, functional magnetic resonance imaging and positron emission tomography have been used to study mental rotation and less attention has been paid to electroencephalogram (EEG) which offers a high time-frequency resolution. The time-frequency analysis is an established method for studying explicit motor imagery. Although hand mental rotation is claimed to involve motor imagery, the time-frequency characteristics of mental rotation have never been compared with those of explicit motor imagery. In this study, time-frequency responses of EEG recorded during explicit motor imagery and during a mental rotation task, inducing implicit motor imagery, were compared. Fifteen right-handed healthy volunteers performed motor imagery of hands in one condition and hand laterality judgement tasks in another while EEG of the whole head was recorded. The hand laterality judgement was the mental rotation task used to induce implicit motor imagery. The time-frequency analysis and sLORETA localisation of the EEG showed that the activities in the sensorimotor areas had similar spatial and time-frequency characteristics in explicit motor imagery and implicit motor imagery conditions. Furthermore this sensorimotor activity was different for the left and for the right hand in both explicit and implicit motor imagery. This result supports that motor imagery is used during mental rotation and that it can be detected and studied with EEG technology. This result should encourage the use of mental rotation of body parts in rehabilitation programmes in a similar manner as motor imagery

The Relative Contribution of High-Gamma Linguistic Processing Stages of Word Production, and Motor Imagery of Articulation in Class Separability of Covert Speech Tasks in EEG Data

Word production begins with high-Gamma automatic linguistic processing functions followed by speech motor planning and articulation. Phonetic properties are processed in both linguistic and motor stages of word production. Four phonetically dissimilar phonemic structures “BA”, “FO”, “LE”, and “RY” were chosen as covert speech tasks. Ten neurologically healthy volunteers with the age range of 21–33 participated in this experiment. Participants were asked to covertly speak a phonemic structure when they heard an auditory cue. EEG was recorded with 64 electrodes at 2048 samples/s. Initially, one-second trials were used, which contained linguistic and motor imagery activities. The four-class true positive rate was calculated. In the next stage, 312 ms trials were used to exclude covert articulation from analysis. By eliminating the covert articulation stage, the four-class grand average classification accuracy dropped from 96.4% to 94.5%. The most valuable features emerge after Auditory cue recognition (~100 ms post onset), and within the 70–128 Hz frequency range. The most significant identified brain regions were the Prefrontal Cortex (linked to stimulus driven executive control), Wernicke’s area (linked to Phonological code retrieval), the right IFG, and Broca’s area (linked to syllabification). Alpha and Beta band oscillations associated with motor imagery do not contain enough information to fully reflect the complexity of speech movements. Over 90% of the most class-dependent features were in the 30-128 Hz range, even during the covert articulation stage. As a result, compared to linguistic functions, the contribution of motor imagery of articulation in class separability of covert speech tasks from EEG data is negligible

Agency and responsibility over virtual movements controlled through different paradigms of brain−computer interface

Agency is the attribution of an action to the self and is a prerequisite for experiencing responsibility over its consequences. Here we investigated agency and responsibility by studying the control of movements of an embodied avatar, via brain computer interface (BCI) technology, in immersive virtual reality. After induction of virtual body ownership by visuomotor correlations, healthy participants performed a motor task with their virtual body. We compared the passive observation of the subject's ‘own’ virtual arm performing the task with (1) the control of the movement through activation of sensorimotor areas (motor imagery) and (2) the control of the movement through activation of visual areas (steady‐state visually evoked potentials). The latter two conditions were carried out using a brain–computer interface (BCI) and both shared the intention and the resulting action. We found that BCI‐control of movements engenders the sense of agency, which is strongest for sensorimotor areas activation. Furthermore, increased activity of sensorimotor areas, as measured using EEG, correlates with levels of agency and responsibility. We discuss the implications of these results for the neural basis of agency

Near-Infrared Spectroscopy for Brain Computer Interfacing

A brain-computer interface (BCI) gives those suffering from neuromuscular impairments a means to interact and communicate with their surrounding environment. A BCI translates physiological signals, typically electrical, detected from the brain to control an output device. A significant problem with current BCIs is the lengthy training periods involved for proficient usage, which can often lead to frustration and anxiety on the part of the user and may even lead to abandonment of the device. A more suitable and usable interface is needed to measure cognitive function more directly. In order to do this, new measurement modalities, signal acquisition and processing, and translation algorithms need to be addressed. This work implements a novel approach to BCI design, using noninvasive near-infrared spectroscopic (NIRS) techniques to develop a userfriendly optical BCI. NIRS is a practical non-invasive optical technique that can detect characteristic haemodynamic responses relating to neural activity. This thesis describes the use of NIRS to develop an accessible BCI system requiring very little user training. In harnessing the optical signal for BCI control an assessment of NIRS signal characteristics is carried out and detectable physiological effects are identified for BCI development. The investigations into various mental tasks for controlling the BCI show that motor imagery functions can be detected using NIRS. The optical BCI (OBCI) system operates in realtime characterising the occurrence of motor imagery functions, allowing users to control a switch - a “Mindswitch”. This work demonstrates the great potential of optical imaging methods for BCI development and brings to light an innovative approach to this field of research

Connectivity Analysis of Brain States and Applications in Brain-Computer Interfaces

Human brain is organized by a large number of functionally correlated but spatially distributed cortical neurons. Cognitive processes are usually associated with dynamic interactions among multiple brain regions. Therefore, the understanding of brain functions requires the inves- tigation of the brain interaction patterns. This thesis contains two main aspects. The first aspect focuses on the neural basis for cognitive processes through the use of brain connectivity analysis. The second part targets on assessing brain connectivity patterns in realistic scenarios, e.g., in-car BCI and stroke patients. In the first part, we explored the neural correlates of error-related brain activity. We recorded scalp electroencephalogram (EEG) from 15 healthy subjects while monitoring the movement of a cursor on a computer screen, yielding particular brain connectivity patterns after monitoring external errors. This supports the presence of common role of medial frontal cortex in coordinating cross-regional activity during brain error processes, independent of their causes, either self-generated or external events. This part also included the investigation of the connectivity during left/right hand motor imagery, including 9 healthy subjects, which demonstrated particular intrahemispheric and interhemispheric information flows in two motor imagery tasks, i.e., the ÎŒ rhythm is highly modulated in intrahemispheric, whereas β and γ are modulated in interhemispheric interactions. This part also explored the neural correlates of reaction time during driving. An experiment with 15 healthy subjects in car simulator was designed, in which they needed to perform lane change to avoid collision with obstacles. Significant neural modulations were found in ERP (event-related potential), PSD (power spectral density), and frontoparietal network, which seems to reflect the underlying information transfer from sensory representation in the parietal cortex to behavioral adjusting in the frontal cortex. In the second part, we first explored the feasibility of using BCI as driving assistant system, in which visual stimuli were presented to evoke error/correct related potentials, and were classified to infer driverâs preferred turning direction. The system was validated in a car simulator with 22 subjects, and 7 joined online tests. The system was also tested in real car, yielding similar brain patterns and comparable classification accuracy. The second part also carried out the brain connectivity analysis in stroke patients.We performed exploratory study to correlate the recovery effects of BCI therapy, through the quantification of connectivity between healthy and lesioned hemispheres. The results indicate the benefits of BCI therapy for stroke patients, i.e., brain connectivity are more similar as healthy patterns, increased (decreased) flow from the damaged (undamaged) to the undamaged (damaged) cortex. Briefly, this thesis presents exploratory studies of brain connectivity analysis, investigating the neural basis of cognitive processes, and its contributions in the decoding phase. In particular, such analysis is not limited to laboratory researches, but also extended to clinical trials and driving scenarios, further supporting the findings observed in the ideal condition

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

Repetitive Transcranial Magnetic Stimulation Affects behavior by Biasing Endogenous Cortical Oscillations

A governing assumption about repetitive transcranial magnetic stimulation (rTMS) has been that it interferes with task-related neuronal activity – in effect, by “injecting noise” into the brain – and thereby disrupts behavior. Recent reports of rTMS-produced behavioral enhancement, however, call this assumption into question. We investigated the neurophysiological effects of rTMS delivered during the delay period of a visual working memory task by simultaneously recording brain activity with electroencephalography (EEG). Subjects performed visual working memory for locations or for shapes, and in half the trials a 10-Hz train of rTMS was delivered to the superior parietal lobule (SPL) or a control brain area. The wide range of individual differences in the effects of rTMS on task accuracy, from improvement to impairment, was predicted by individual differences in the effect of rTMS on power in the alpha-band of the EEG (∼10 Hz): a decrease in alpha-band power corresponded to improved performance, whereas an increase in alpha-band power corresponded to the opposite. The EEG effect was localized to cortical sources encompassing the frontal eye fields and the intraparietal sulcus, and was specific to task (location, but not object memory) and to rTMS target (SPL, not control area). Furthermore, for the same task condition, rTMS-induced changes in cross-frequency phase synchrony between alpha- and gamma-band (>40 Hz) oscillations predicted changes in behavior. These results suggest that alpha-band oscillations play an active role cognitive processes and do not simply reflect absence of processing. Furthermore, this study shows that the complex effects of rTMS on behavior can result from biasing endogenous patterns of network-level oscillations