Neurophysiological correlates of preparation for action measured by electroencephalography

The optimal performance of an action depends to a great extend on the ability of a person to prepare in advance the appropriate kinetic and kinematic parameters at a specific point in time in order to meet the demands of a given situation and to foresee its consequences to the surrounding environment. In the research presented in this thesis, I employed high-density electroencephalography in order to study the neural processes underlying preparation for action. A typical way for studying preparation for action in neuroscience is to divide it in temporal preparation (when to respond) and event preparation (what response to make). In Chapter 2, we identified electrophysiological signs of implicit temporal preparation in a task where such preparation was not essential for the performance of the task. Electrophysiological traces of implicit timing were found in lateral premotor, parietal as well as occipital cortices. In Chapter 3, explicit temporal preparation was assessed by comparing anticipatory and reactive responses to periodically or randomly applied external loads, respectively. Higher (pre)motor preparatory activity was recorded in the former case, which resulted in lower post-load motor cortex activation and consequently to lower long-latency reflex amplitude. Event preparation was the theme of Chapter 4, where we introduced a new method for studying (at the source level) the generator mechanisms of lateralized potentials related to response selection, through the interaction with steady-state somatosensory responses. Finally, in Chapter 5 we provided evidence for the existence of concurrent and mutually inhibiting representations of multiple movement options in premotor and primary motor areas.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Using novel stimuli and alternative signal processing techniques to enhance BCI paradigms

A Brain-Computer Interface (BCI) is a device that uses the brain activity of a person as an input to select desired outputs on a computer. BCIs that use surface electroencephalogram (EEG) recordings as their input are the least invasive but also suffer from a very low signal-to-noise ratio (SNR) due to the very low amplitude of the person’s brain activity and the presence of many signal artefacts and background noise. This can be compensated for by subjecting the signals to extensive signal processing, and by using stimuli to trigger a large but consistent change in the signal – these changes are called evoked potentials. The method used to stimulate the evoked potential, and introduce an element of conscious selection in order to allow the user’s intent to modify the evoked potential produced, is called the BCI paradigm. However, even with these additions the performance of BCIs used for assistive communication and control is still significantly below that of other assistive solutions, such as keypads or eye-tracking devices. This thesis examines the paradigm and signal processing components of BCIs and puts forward several methods meant to enhance BCIs’ performance and efficiency. Firstly, two novel signal processing methods based on Empirical Mode Decomposition (EMD) were developed and evaluated. EMD is a technique that divides any oscillating signal into groups of frequency harmonics, called Intrinsic Mode Functions (IMFs). Furthermore, by using Takens’ theorem, a single channel of EEG can be converted into a multi-temporal channel signal by transforming the channel into multiple snapshots of its signal content in time using a series of delay vectors. This signal can then be decomposed into IMFs using a multi-channel variation of EMD, called Multi-variate EMD (MEMD), which uses the spatial information from the signal’s neighbouring channels to inform its decomposition. In the case of a multi-temporal channel signal, this allows the temporal dynamics of the signal to be incorporated into the IMFs. This is called Temporal MEMD (T-MEMD). The second signal processing method based on EMD decomposed both the spatial and temporal channels simultaneously, allowing both spatial and temporal dynamics to be incorporated into the resulting IMFs. This is called Spatio-temporal MEMD (ST-MEMD). Both methods were applied to a large pre-recorded Motor Imagery BCI dataset along with EMD and MEMD for comparison. These results were also compared to those from other studies in the literature that had used the same dataset. T-MEMD performed with an average classification accuracy of 70.2%, performing on a par with EMD that had an average classification accuracy of 68.9%. Both ST-MEMD and MEMD outperformed them with ST-MEMD having an average classification accuracy of 73.6%, and MEMD having an average classification accuracy of 75.3%. The methods containing spatial dynamics, i.e. MEMD and ST-MEMD, outperformed those with only temporal dynamics, i.e. EMD and T-MEMD. The two methods with temporal dynamics each performed on a par with the non-temporal method that had the same level of spatial dynamics. This shows that only the presence of spatial dynamics resulted in a performance increase. This was concluded to be because the differences between the classes of motor-imagery are inherently spatial in nature, not temporal. Next a novel BCI paradigm was developed based on the standard Steady-state Somatosensory Evoked Potential (SSSEP) BCI paradigm. This paradigm uses a tactile stimulus applied to the skin at a certain frequency, generating a resonance signal in the brain’s activity. If two stimuli of different frequency are applied, two resonance signals will be present. However, if the user attends one stimulus over the other, its corresponding SSSEP will increase in amplitude. Unfortunately these changes in amplitude can be very minute. To counter this, a stimulus amplitude and frequency of the vibrotactile stimuli. It was hypothesised that if the stimuli generator was constructed that could alter the were of the same frequency, but one’s amplitude was just below the user’s conscious level of perception and the other was above it, the changes in the SSSEP between classes would be the same as those between an SSSEP being generated and neutral EEG, with differences in α activity between the low-amplitude SSSEP and neutral activity due to the differences in the user’s level of concentration from attending the low-amplitude stimulus. The novel SSSEP BCI paradigm performed on a par with the standard paradigm with an average 61.8% classification accuracy over 16 participants, compared to an average 63.3% classification accuracy respectively, indicating that the hypothesis was false. However, the large presence of electro-magnetic interference (EMI) in the EEG recordings may have compromised the data. Many different noise suppression methods were applied to the stimulus device and the data, and whilst the EMI artefacts were reduced in magnitude they were not eliminated completely. Even with the noise the standard SSSEP stimulus paradigm performed on a par with studies that used the same paradigm, indicating that the results may not have been invalidated by the EMI. Overall the thesis shows that motor-imagery signals are inherently spatial in difference, and that the novel methods of T-MEMD and ST-MEMD may yet out-perform the existing methods of EMD and MEMD if applied to signals that are temporal in nature, such as functional Magnetic Resonance Imaging (fMRI). Whilst the novel SSSEP paradigm did not result in an increase in performance, it highlighted the impact of EMI from stimulus equipment on EEG recordings and potentially confirmed that the amplitude of SSEP stimuli is a minor factor in a BCI paradigm

Cognitive Assessment and Rehabilitation of subjects with Traumatic Brain Injury

This thesis regards the study and the development of new cognitive assessment and rehabilitation techniques of subjects with traumatic brain injury (TBI). In particular, this thesis i) provides an overview about the state of art of this new assessment and rehabilitation technologies, ii) suggests new methods for the assessment and rehabilitation and iii) contributes to the explanation of the neurophysiological mechanism that is involved in a rehabilitation treatment. Some chapters provide useful information to contextualize TBI and its outcome; they describe the methods used for its assessment/rehabilitation. The other chapters illustrate a series of experimental studies conducted in healthy subjects and TBI patients that suggest new approaches to assessment and rehabilitation. The new proposed approaches have in common the use of electroencefalografy (EEG). EEG was used in all the experimental studies with a different purpose, such as diagnostic tool, signal to command a BCI-system, outcome measure to evaluate the effects of a treatment, etc. The main achieved results are about: i) the study and the development of a system for the communication with patients with disorders of consciousness. It was possible to identify a paradigm of reliable activation during two imagery task using EEG signal or EEG and NIRS signal; ii) the study of the effects of a neuromodulation technique (tDCS) on EEG pattern. This topic is of great importance and interest. The emerged founding showed that the tDCS can manipulate the cortical network activity and through the research of optimal stimulation parameters, it is possible move the working point of a neural network and bring it in a condition of maximum learning. In this way could be possible improved the performance of a BCI system or to improve the efficacy of a rehabilitation treatment, like neurofeedback

Enhancement and optimization of a multi-command-based brain-computer interface

Brain-computer interfaces (BCI) assist disabled person to control many appliances without any physically interaction (e.g., pressing a button). SSVEP is brain activities elicited by evoked signals that are observed by visual stimuli paradigm. In this dissertation were addressed the problems which are oblige more usability of BCI-system by optimizing and enhancing the performance using particular design. Main contribution of this work is improving brain reaction response depending on focal approaches

Improving classification of error related potentials using novel feature extraction and classification algorithms for an assistive robotic device

We evaluated the proposed feature extraction algorithm and the classifier, and we showed that the performance surpassed the state of the art algorithms in error detection. Advances in technology are required to improve the quality of life of a person with a severe disability who has lost their independence of movement in their daily life. Brain-computer interface (BCI) is a possible technology to re-establish a sense of independence for the person with a severe disability through direct communication between the brain and an electronic device. To enhance the symbiotic interface between the person and BCI its accuracy and robustness should be improved across all age groups. This thesis aims to address the above-mentioned issue by developing a novel feature extraction algorithm and a novel classification algorithm for the detection of erroneous actions made by either human or BCI. The research approach evaluated the state of the art error detection classifier using data from two different age groups, young and elderly. The performance showed a statistical difference between the aforementioned age groups; therefore, there needs to be an improvement in error detection and classification. The results showed that my proposed relative peak feature (RPF) and adaptive decision surface (ADS) classifier outperformed the state of the art algorithms in detecting errors using EEG for both elderly and young groups. In addition, the novel classification algorithm has been applied to motor imagery to improve the detection of when a person imagines moving a limb. Finally, this thesis takes a brief look at object recognition for a shared control task of identifying utensils in cooperation with a prosthetic robotic hand

The role of simulation in developing and designing applications for 2-class motor imagery brain-computer interfaces

A Brain-Computer Interface (BCI) can be used by people with severe physical disabilities such as Locked-in Syndrome (LiS) as a channel of input to a computer. The time-consuming nature of setting up and using a BCI, together with individual variation in performance and limited access to end users makes it difficult to employ techniques such as rapid prototyping and user centred design (UCD) in the design and development of applications. This thesis proposes a design process which incorporates the use of simulation tools and techniques to improve the speed and quality of designing BCI applications for the target user group. Two different forms of simulation can be distinguished: offline simulation aims to make predictions about a user’s performance in a given application interface given measures of their baseline control characteristics, while online simulation abstracts properties of inter- action with a BCI system which can be shown to, or used by, a stakeholder in real time. Simulators that abstract properties of BCI control at different levels are useful for different purposes. Demonstrating the use of offline simulation, Chapter 3 investigates the use of finite state machines (FSMs) to predict the time to complete tasks given a particular menu hierarchy, and compares offline predictions of task performance with real data in a spelling task. Chapter 5 aims to explore the possibility of abstracting a user’s control characteristics from a typical calibration task to predict performance in a novel control paradigm. Online simulation encompasses a range of techniques from low-fidelity prototypes built using paper and cardboard, to computer simulation models that aim to emulate the feel of control of using a BCI without actually needing to put on the BCI cap. Chapter 4 details the develop- ment and evaluation of a high fidelity BCI simulator that models the control characteristics of a BCI based on the motor-imagery (MI) paradigm. The simulation tools and techniques can be used at different stages of the application design process to reduce the level of involvement of end users while at the same time striving to employ UCD principles. It is argued that prioritising the level of involvement of end users at different stages in the design process is an important strategy for design: end user input is paramount particularly at the initial user requirements stage where the goals that are important for the end user of the application can be ascertained. The interface and specific interaction techniques can then be iteratively developed through both real and simulated BCI with people who have no or less severe physical disabilities than the target end user group, and evaluations can be carried out with end users at the final stages of the process. Chapter 6 provides a case study of using the simulation tools and techniques in the development of a music player application. Although the tools discussed in the thesis specifically concern a 2-class Motor Imagery BCI which uses the electroencephalogram (EEG) to extract brain signals, the simulation principles can be expected to apply to a range of BCI systems