Enhancing brain-computer interfacing through advanced independent component analysis techniques

A Brain-computer interface (BCI) is a direct communication system between a brain and an external device in which messages or commands sent by an individual do not pass through the brain’s normal output pathways but is detected through brain signals. Some severe motor impairments, such as Amyothrophic Lateral Sclerosis, head trauma, spinal injuries and other diseases may cause the patients to lose their muscle control and become unable to communicate with the outside environment. Currently no effective cure or treatment has yet been found for these diseases. Therefore using a BCI system to rebuild the communication pathway becomes a possible alternative solution. Among different types of BCIs, an electroencephalogram (EEG) based BCI is becoming a popular system due to EEG’s fine temporal resolution, ease of use, portability and low set-up cost. However EEG’s susceptibility to noise is a major issue to develop a robust BCI. Signal processing techniques such as coherent averaging, filtering, FFT and AR modelling, etc. are used to reduce the noise and extract components of interest. However these methods process the data on the observed mixture domain which mixes components of interest and noise. Such a limitation means that extracted EEG signals possibly still contain the noise residue or coarsely that the removed noise also contains part of EEG signals embedded. Independent Component Analysis (ICA), a Blind Source Separation (BSS) technique, is able to extract relevant information within noisy signals and separate the fundamental sources into the independent components (ICs). The most common assumption of ICA method is that the source signals are unknown and statistically independent. Through this assumption, ICA is able to recover the source signals. Since the ICA concepts appeared in the fields of neural networks and signal processing in the 1980s, many ICA applications in telecommunications, biomedical data analysis, feature extraction, speech separation, time-series analysis and data mining have been reported in the literature. In this thesis several ICA techniques are proposed to optimize two major issues for BCI applications: reducing the recording time needed in order to speed up the signal processing and reducing the number of recording channels whilst improving the final classification performance or at least with it remaining the same as the current performance. These will make BCI a more practical prospect for everyday use. This thesis first defines BCI and the diverse BCI models based on different control patterns. After the general idea of ICA is introduced along with some modifications to ICA, several new ICA approaches are proposed. The practical work in this thesis starts with the preliminary analyses on the Southampton BCI pilot datasets starting with basic and then advanced signal processing techniques. The proposed ICA techniques are then presented using a multi-channel event related potential (ERP) based BCI. Next, the ICA algorithm is applied to a multi-channel spontaneous activity based BCI. The final ICA approach aims to examine the possibility of using ICA based on just one or a few channel recordings on an ERP based BCI. The novel ICA approaches for BCI systems presented in this thesis show that ICA is able to accurately and repeatedly extract the relevant information buried within noisy signals and the signal quality is enhanced so that even a simple classifier can achieve good classification accuracy. In the ERP based BCI application, after multichannel ICA the data just applied to eight averages/epochs can achieve 83.9% classification accuracy whilst the data by coherent averaging can reach only 32.3% accuracy. In the spontaneous activity based BCI, the use of the multi-channel ICA algorithm can effectively extract discriminatory information from two types of singletrial EEG data. The classification accuracy is improved by about 25%, on average, compared to the performance on the unpreprocessed data. The single channel ICA technique on the ERP based BCI produces much better results than results using the lowpass filter. Whereas the appropriate number of averages improves the signal to noise rate of P300 activities which helps to achieve a better classification. These advantages will lead to a reliable and practical BCI for use outside of the clinical laboratory

USEQ: A Short Questionnaire for Satisfaction Evaluation of Virtual Rehabilitation Systems

[EN] New emerging technologies have proven their efficacy in aiding people in their rehabilitation. The tests that are usually used to evaluate usability (in general) or user satisfaction (in particular) of this technology are not specifically focused on virtual rehabilitation and patients. The objective of this contribution is to present and evaluate the USEQ (User Satisfaction Evaluation Questionnaire). The USEQ is a questionnaire that is designed to properly evaluate the satisfaction of the user (which constitutes part of usability) in virtual rehabilitation systems. Forty patients with balance disorders completed the USEQ after their first session with ABAR (Active Balance Rehabilitation), which is a virtual rehabilitation system that is designed for the rehabilitation of balance disorders. Internal consistency analysis and exploratory factor analysis were carried out to identify the factor structure of the USEQ. The six items of USEQ were significantly associated with each other, and the Cronbach alpha coefficient for the questionnaire was 0.716. In an analysis of the principal components, a one-factor solution was considered to be appropriate. The findings of the study suggest that the USEQ is a reliable questionnaire with adequate internal consistency. With regard to patient perception, the patients found the USEQ to be an easy-to-understand questionnaire with a convenient number of questions.Gil-Gómez, J.; Manzano-Hernández, P.; Albiol-Perez, S.; Aula-Valero, C.; Gil Gómez, H.; Lozano Quilis, JA. (2017). USEQ: A Short Questionnaire for Satisfaction Evaluation of Virtual Rehabilitation Systems. Sensors. 17(7):1-12. https://doi.org/10.3390/s17071589S112177BEVAN, N. (2001). International standards for HCI and usability. International Journal of Human-Computer Studies, 55(4), 533-552. doi:10.1006/ijhc.2001.0483Abran, A., Khelifi, A., Suryn, W., & Seffah, A. (2003). 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The Laryngoscope, 124(8), 1887-1894. doi:10.1002/lary.24566Meldrum, D., Glennon, A., Herdman, S., Murray, D., & McConn-Walsh, R. (2011). Virtual reality rehabilitation of balance: assessment of the usability of the Nintendo Wii®Fit Plus. Disability and Rehabilitation: Assistive Technology, 7(3), 205-210. doi:10.3109/17483107.2011.616922Kalawsky, R. S. (1999). VRUSE—a computerised diagnostic tool: for usability evaluation of virtual/synthetic environment systems. Applied Ergonomics, 30(1), 11-25. doi:10.1016/s0003-6870(98)00047-7Cameirao, M. S., Bermudez i Badia, S., Duarte Oller, E., & Verschure, P. F. (2010). Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation. Journal of NeuroEngineering and Rehabilitation, 7(1), 48. doi:10.1186/1743-0003-7-48Regenbrecht, H., Hoermann, S., McGregor, G., Dixon, B., Franz, E., Ott, C., … Hoermann, J. (2012). Visual manipulations for motor rehabilitation. 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Valutazione degli stati mentali attraverso l'utilizzo di interfacce cervello-computer passive

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.La valutazione delle funzioni cognitive ha l’obbiettivo di ottenere informazioni sullo stato mentale attuale dell'utente, attraverso la decodifica dei segnali cerebrali. Negli ultimi anni, questo approccio ha consentito di indagare informazioni preziose sugli aspetti cognitivi riguardanti l'interazione tra l’uomo ed il mondo esterno. In base a queste considerazioni, recentemente si è considerata in letteratura la possibilità di utilizzare le interfacce cervello computer passive (BCI passivi) per interagire con dispositivi esterni, sfruttando l’attività spontanea dell’utente. L'obiettivo di questa tesi è quello di dimostrare come le interfacce cervello computer passive possano essere utilizzate per valutare lo stato mentale dell’utente, al fine di migliorare l'interazione uomo-macchina. Sono stati presentati due studi principali. Il primo ha l’obbiettivo di investigare le variazioni morfologiche dei potenziali evento correlati (ERP), al fine di associarle agli stati mentali dell’utente (es. attenzione, carico di lavoro mentale) durante l’utilizzo di BCI reattive, e come predittori delle performance raggiunte dai soggetti. Nel secondo studio è stato sviluppato e validato un sistema BCI passivo in grado di stimare il carico di lavoro mentale dell'utente durante task operative, attraverso la combinazione del segnale elettroencefalografico (EEG) ed elettrocardiografico (ECG). Quest'ultimo studio è stato effettuato simulando uno scenario operativo, in cui il verificarsi di errori da parte dell’operatore o il calo di prestazioni poteva avere conseguenze importanti. I risultati hanno mostrato che il sistema proposto è in grado di discriminare il carico di lavoro mentale percepito dall’utente su tre livelli di difficoltà, garantendo un’elevata affidabilità