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

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Issues in the processing and analysis of functional NIRS imaging and a contrast with fMRI findings in a study of sensorimotor deactivation and connectivity

Includes abstract.~Includes bibliographical references.The first part of this thesis examines issues in the processing and analysis of continuous wave functional linear infrared spectroscopy (fNIRS) of the brain usung the DYNOT system. In the second part, the same sensorimotor experiment is carried out using functional magnetic resonance imaging (fMRI) and near infrared spectroscopy in eleven of the same subjects, to establish whether similar results can be obtained at the group level with each modality. Various techniques for motion artefact removal in fNIRS are compared. Imaging channels with negligible distance between source and detector are used to detect subject motion, and in data sets containing deliberate motion artefacts, independent component analysis and multiple-channel regression are found to improve the signal-to-noise ratio

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

When the Brain Plays a Game: Neural Responses to Visual Dynamics during Naturalistic Visual Tasks

Many day-to-day tasks involve processing of complex visual information in a continuous stream. While much of our knowledge on visual processing has been established from reductionist approaches in lab-controlled settings, very little is known about the processing of complex dynamic stimuli experienced in everyday scenarios. Traditional investigations employ event-related paradigms that involve presentation of simple stimuli at select locations in visual space and discrete moments in time. In contrast, visual stimuli in real-life are highly dynamic, spatially-heterogeneous, and semantically rich. Moreover, traditional experiments impose unnatural task constraints (e.g., inhibited saccades), thus, it is unclear whether theories developed under the reductionist approach apply in naturalistic settings. Given these limitations, alternative experimental paradigms and analysis methods are necessary. Here, we introduce a new approach for investigating visual processing, applying the system identification (SI) framework. We investigate the modulation of stimulus-evoked responses during a naturalistic task (i.e., kart race game) using non-invasive scalp recordings. In recent years, multivariate modeling approaches have become increasingly popular for assessing neural response to naturalistic stimuli. Encoding models use stimulus patterns to predict brain responses and decoding models use patterns of brain responses to predict stimulus that drove these responses. In this dissertation, we employ a hybrid method that “encodes” the stimulus to predict “decoded” brain responses. Using this approach, we measure the stimulus-response correlation (SRC), i.e. temporal correlation of neural response and dynamic stimulus. This SRC can be used to assess the strength of stimulus-evoked activity to uniquely experienced naturalistic stimulus. To demonstrate this, we measured the SRC during a kart race videogame. We find that SRC increased with active play of the game, suggesting that stimulus-evoked activity is modulated by the visual task demands. Furthermore, we analyzed the selectivity of neural response across the visual space. While it is well-established that neural response is spatially selective to discrete stimulus, it is unclear whether this is true during naturalistic stimulus presentation. To assess this, we measured the correlation of neural response with optical flow magnitude at individual locations on the screen during the videogame. We find that the SRC is greater for locations in space that are task-relevant, enhancing during active play. Moreover, the spatial selectivity differs across scalp locations, which suggest that individual brain regions are spatially selective to different visual dynamics. In summary, we leverage the SI framework to investigate visual processing during a naturalistic stimulus presentation, extending visual research to ecologically valid paradigms. Moreover, we demonstrate spatial selectivity of neural response that are task-relevant. Overall, our findings shed new insights about the stimulus-evoked neural response to visual dynamics during a uniquely experienced naturalistic visual task. Taken together, this dissertation work makes a significant contribution towards understanding how visual dynamics and task behavior affects neural responses in naturalistic conditions

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

Evidence from London taxi drivers of hierarchical route planning in a real-world environment

The ability to navigate a spatial environment strongly depends on how well individuals learn, represent and make use of their knowledge about the environment. In the past, research investigated these aspects separately and often in a virtual environment. The current work studied these three aspects of navigation in a real-real world setting to understand how humans navigate naturally in a complex, urban environment like London, UK. Of particular interest was to determine if there was evidence of hierarchical representations during route planning as found in previous behavioural, neuroscientific or computational studies. Most past studies have explored knowledge for simplistic environments or fragmented knowledge of real-world environments. By contrast, licensed London taxi drivers acquire a unique, almost perfect mental representation of the street network, the location of places and the traffic rules that apply to it. Here, the rare knowledge of these navigation experts was explored in three studies with novel approaches. First, to gain an understanding of the training process of unqualified taxi drivers, information from an interview with a teacher, training lessons and study material was collected, summarised and reported. A range of learning strategies was identified that was linked to theoretical, map-based learning and practical, in-situ experiences of London and pointed towards a segmented planning of routes through subgoal selection. Second, a potential mental segregation of London was studied with qualified taxi drivers through boundary drawings of specific London districts with a paper map to understand a potential hierarchical representation. Higher agreement was found for geographical structures and topically distinct districts surrounded by a linear, almost rectangular street network, whereas agreement was lowest for irregularly shaped districts with similarities to neighbouring areas. Finally, taxi drivers were asked to plan and then verbally recall each street they would take along routes between selected origin destination pairs. Audio recordings of these routes made it possible to relate the response times between individual streets to specific street network properties. The analysis using a linear mixed model indicated slower responses at upcoming turns and entering main roads, whereas boundary streets were recalled faster, as were finial streets when compared to initial street. No effects of Euclidean distance or detours were found. Observations from the training process indicate that a potential segregation of the environment, which might impact on later route planning, might be formed already through specific learning strategies. Faster response times for boundary streets support models in which planning is hierarchical. These findings extend past work on route planning in lab-based networks to real-world city street networks and highlight avenues for future research to explore and make use of real-world data