Biomedical Engineering - Theses
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ItemDecoding Sensorimotor Rhythms for Brain-Computer Interfaces( 2021)Brain-computer interfaces (BCIs) have great potential to improve the quality of life for people with severe motor disabilities. By measuring and interpreting neural activity, BCIs can predict and express intention through an external computerised device. This creates an alternative mechanism for communication and control for people with paralysis. Volitional changes in oscillatory activity near the sensorimotor cortex, known as sensorimotor rhythms (SMRs), can be measured with invasive techniques, such as electrocorticography (ECoG), or with non-invasive techniques, such as electroencephalography (EEG). This work explored novel approaches for decoding SMRs from EEG with the aim of utilising neurophysiological principles. This thesis also investigated the viability of vascular electrocorticography (vECoG) as a SMR-based BCI modality. The common spatial patterns (CSP) algorithm is used to linearly combine information from multiple EEG electrodes in order to accentuate SMR activity. Typical patterns of SMR activity derived by this method were characterised using publicly available EEG datasets. It was found that both neurophysiologically probable and improbable patterns of activity were commonly extracted and that selecting for, and adapting to, neurophysiologically probable activity could improve decoding performance. These findings highlight the importance of considering spatial filter adaptation in EEG BCI decoder design. The range of decoding algorithms available to BCI practitioners is extensive and diverse. A universal method for explaining the predictions of EEG decoders in terms of neurophysiologically relevant factors was investigated. The validity of the explanations was demonstrated using simulated EEG data. The method was also employed to compare the behaviour of four categories of decoders using real, pre-recorded EEG data. The results indicated that all decoders were able to harness neurophysiologically plausible electrodes and cortical sources to make accurate predictions. However, the influence of artifactual activity was also found to contribute to high decoder accuracy. These findings emphasise the need to understand the predictive behaviour of decoders and the proposed method may be useful a tool to help BCI researchers achieve this understanding. Vascular electrocorticography (vECoG) measures intracranial neural activity by chronically implanting a stent-electrode array within the brain vasculature. By omitting the need to penetrate the skull, this minimally invasive technique has the potential to safely record high fidelity neural information and be used as a long-term, clinically useful BCI. Data from the first-in-human clinical trial of the Stentrode was used to characterise vECoG signal quality. Participant-specific re-referencing was shown to improve signal quality and the maximum bandwidth of the signal was found to be consistent with previous animal studies. A multiclass, online SMR decoder was also implemented and tested with a single participant. Discriminatory activity from multiple motor imagery classes could be observed, however, signal non-stationarity affected online decoding performance. Together, the signal quality and multiclass decoding results suggest that vECoG has significant potential as a recording modality for a clinically useful BCI. Overall, the findings presented in this thesis contribute to two key facets of SMR BCI research. In terms of decoding SMRs from EEG, they demonstrate the need to harness neurophysiological phenomena, avoid contamination from artifactual activity and improve the interpretability of complex decoding algorithms. Furthermore, the vECoG results add to the growing body of evidence implicating this modality as a beneficial way for interfacing with the brain in a minimally invasive manner.