A Comparison of Movement-Related Cortical Potentials and Their Application in Brain-Computer Interfaces for Autism Spectrum Disorder

Brain-computer interfaces have the potential to improve the lives of many populations who benefit from neurofeedback. Autism Spectrum Disorder is a condition experienced by many and its deficits are potentially improved for some using brain-computer interface technology. Various techniques have already been used to illustrate improvements in ASD across different brain signals and interactive interfaces. In particular, movement-related cortical potentials are related to executive functioning of movement and have been shown to be successful in other systems. This thesis investigates the effect of Autism Spectrum Disorder in adults on how movement-related cortical potentials are elicited in the brain compared to neurotypical populations to determine whether the motor systems that elicit such signals are abnormally functioning, and as a result whether they may be improved with neurofeedback. In addition to understanding the EEG response for people with ASD to brain-computer interfaces, it is important to gain insights into their perception of such technologies. This thesis also examines how people with ASD perceive different potential brain-computer interfaces. Quantitative and qualitative data was collected and analysed across three different interfaces (auditory, visual, and haptic) and two different tasks (real movement and imagined movement execution). The EEG results show statistically significant differences in the elicitation of movement-related cortical potentials (MRCPs) between the autistic and neurotypical group, thus indicating possible underlying abnormalities in the motor systems being activated. The features of MRCP were much smaller in amplitude in the ASD group, suggesting that fewer neurons are being recruited for movement-based actions. Since other studies have demonstrated success when improving MRCPs in populations suffering from Parkinson’s and stroke, it is thus inferred that such neurofeedback may also benefit those with Autism Spectrum Disorder. While there were no statistical differences regarding EEG-related performance for different modalities, qualitative results suggest common themes regarding people with ASD’s subjective perceptions, including the need for feedback on performance and strong preferences for different types of modalities. These results emphasize the importance of considering both quantitative and qualitative data when designing brain-computer interfaces for these populations. This research demonstrates an opportunity to use MRCP-based neurofeedback to help populations with ASD, as well as emphasizes the importance and insights of capturing qualitative data in the process

Age-Related Changes in Vibro-Tactile EEG Response and Its Implications in BCI Applications: A Comparison Between Older and Younger Populations

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The rapid increase in the number of older adults around the world is accelerating research in applications to support age-related conditions, such as brain-computer interface (BCI) applications for post-stroke neurorehabilitation. The signal processing algorithms for electroencephalogram (EEG) and other physiological signals that are currently used in BCI have been developed on data from much younger populations. It is unclear how age-related changes may affect the EEG signal and therefore the use of BCI by older adults. This research investigated the EEG response to vibro-tactile stimulation from 11 younger (21.7 ± 2.76 years old) and 11 older (72.0 ± 8.07 years old) subjects. The results showed that: 1) the spatial patterns of cortical activation in older subjects were significantly different from those of younger subjects, with markedly reduced lateralization; 2) there is a general power reduction of the EEG measured from older subjects. The average left vs. right BCI performance accuracy of older subjects was 66.4 ± 5.70%, 15.9% lower than that of the younger subjects (82.3 ± 12.4%) and statistically significantly different (t(10) = -3.57, p = 0.005). Future research should further investigate age-differences that may exist in electrophysiology and take these into consideration when developing applications that target the older population.This work was supported in part by the University of Waterloo’s Starter Grant 203859, in part by Schlegel Research Chair funds, and in part by the Early Researcher Award from the Ministry of Research, Innovation and Science of Ontario under Grant ER17-13-183

Електроенцефалографски сигнали за управљање рачунарским интерфејсом у неурорехабилитацији

Мозак-рачунар интерфејс (МоРИ) системи могу искористити карактеристичне промене мождане активности корисника као контролне сигнале уређаја (рачунара). Различити ментални задаци или спољашњи стимулуси (визуелни, аудитивни или соматосензорни) индукују промене које су кодиране у спонтаној неуралној активности. Генерисане промене се могу идентификовати мерењем можданих сигнала који представљају директну или индиректну меру електричне активности мозга...Brain Computer Interface (BCI) systems can use characteristic brain neural alterations as control signals of the device/computer. Various mental tasks or external stimulation (visual, auditory or somatosensory) induce changes which are embedded in the spontaneous neural activity. Generated changes can be extracted and identified from the brain-signal recordings that represent the (direct or indirect) measure of electrical neural activity..

Ongoing temporal dynamics of broadband EEG during movement intention for BCI

Brain Computer Interface (BCI) empowers individuals with severe movement impairing conditions to interact with the computers directly by their thoughts, without the involvement of any motor pathways. Motor-based BCIs can offer intuitive control by merely intending to move. Hence, to develop effective motor-based non-invasive BCIs, it is essential to understand the mechanisms of neural processes involved in motor command generation in electroencephalography (EEG). The EEG consists of complex narrowband oscillatory and broadband arrhythmic processes. However, there is more focus on the oscillations in different frequency bands for studying motor command generation in the literature. The narrowband processes such as event-related (de)synchronisation (ERD/S) and movement-related cortical potential (MRCP) are commonly used for movement detection. Analysis of these narrowband EEG components disregards the information existing in the rest of the frequencies and their dynamics. Hence, this thesis investigates various facets of previously unexplored temporal dynamics of neuronal processes in the broadband arrhythmic EEG to fill the gap in the knowledge of motor command generation on a single trial basis in the BCI framework. The temporal dynamics of the broadband EEG were characterised by the decay of its autocorrelation. The autocorrelation decayed according to the power-law resulting in the longrange temporal correlations (LRTC). The instantaneous ongoing changes in the broadband LRTC were uniquely quantified by the Hurst exponent on very short EEG sliding windows. There was an increase in the temporal dependencies in the EEG leading to slower decay of autocorrelation during the movement and significant increase in the LRTC (p<0.05). Different types of temporal dependencies in the broadband EEG were comprehensively examined further by modelling the long and short-range correlations together using autoregressive fractionally integrated moving average model (ARFIMA). The short-range correlations also changed significantly (p<0.05) during the movement. These ongoing changes in the dynamics of the broadband EEG were able to predict the movement 1 s before its onset with accuracy higher than ERD and MRCP. The LRTCs were robust across participants and did not require determination of participant specific parameters such as most responsive spectral or spatial components

Identification of audio evoked response potentials in ambulatory EEG data

Electroencephalography (EEG) is commonly used for observing brain function over a period of time. It employs a set of invasive electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers and clinicians to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of undesirable signals, which are known as artefacts, contaminate the EEG signal. EEG and ERP signals are very small (in the order of microvolts); they are often obscured by artefacts with much larger amplitudes in the order of millivolts. This greatly increases the difficulty of interpreting EEG and ERP signals.Typically, ERPs are observed by averaging EEG measurements made with many repetitions of the stimulus. The average may require many tens of repetitions before the ERP signal can be observed with any confidence. This greatly limits the study and useof ERPs. This project explores more sophisticated methods of ERP estimation from measured EEGs. An Optimal Weighted Mean (OWM) method is developed that forms a weighted average to maximise the signal to noise ratio in the mean. This is developedfurther into a Bayesian Optimal Combining (BOC) method where the information in repetitions of ERP measures is combined to provide a sequence of ERP estimations with monotonically decreasing uncertainty. A Principal Component Analysis (PCA) isperformed to identify the basis of signals that explains the greatest amount of ERP variation. Projecting measured EEG signals onto this basis greatly reduces the noise in measured ERPs. The PCA filtering can be followed by OWM or BOC. Finally, crosschannel information can be used. The ERP signal is measured on many electrodes simultaneously and an improved estimate can be formed by combining electrode measurements. A MAP estimate, phrased in terms of Kalman Filtering, is developed using all electrode measurements.The methods developed in this project have been evaluated using both synthetic and measured EEG data. A synthetic, multi-channel ERP simulator has been developed specifically for this project.Numerical experiments on synthetic ERP data showed that Bayesian Optimal Combining of trial data filtered using a combination of PCA projection and Kalman Filtering, yielded the best estimates of the underlying ERP signal. This method has been applied to subsets of real Ambulatory Electroencephalography (AEEG) data, recorded while participants performed a range of activities in different environments. From this analysis, the number of trials that need to be collected to observe the P300 amplitude and delay has been calculated for a range of scenarios

User-Centered Design Strategies for Clinical Brain-Computer Interface Assistive Technology Devices

Although in the past 50 years significant advances based on research of brain-computer interface (BCI) technology have occurred, there is a scarcity of BCI assistive technology devices at the consumer level. This multiple case study explored user-centered clinical BCI device design strategies used by computer scientists designing BCI assistive technologies to meet patient-centered outcomes. The population for the study encompassed computer scientists experienced with clinical BCI assistive technology design located in the midwestern, northeastern, and southern regions of the United States, as well as western Europe. The multi-motive information systems continuance model was the conceptual framework for the study. Interview data were collected from 7 computer scientists and 28 archival documents. Guided by the concepts of user-centered design and patient-centered outcomes, thematic analysis was used to identify codes and themes related to computer science and the design of BCI assistive technology devices. Notable themes included customization of clinical BCI devices, consideration of patient/caregiver interaction, collective data management, and evolving technology. Implications for social change based on the findings from this research include focus on meeting individualized patient-centered outcomes; enhancing collaboration between researchers, caregivers, and patients in BCI device development; and reducing the possibility of abandonment or disuse of clinical BCI assistive technology devices