2 research outputs found

    Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI

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    One of the main challenges in studying brain signals is the large size of the data due to the use of many electrodes and the time-consuming sampling. Choosing the right dimensional reduction method can lead to a reduction in the data processing time. Evolutionary algorithms are one of the methods used to reduce the dimensions in the field of EEG brain signals, which have shown better performance than other common methods. In this article, (1) a new Bond Graph algorithm (BGA) is introduced that has demonstrated better performance on eight benchmark functions compared to genetic algorithm and particle swarm optimization. Our algorithm has fast convergence and does not get stuck in local optimums. (2) Reductions of features, electrodes, and the frequency range have been evaluated simultaneously for brain signals (left-handed and right-handed). BGA and other algorithms are used to reduce features. (3) Feature extraction and feature selection (with algorithms) for time domain, frequency domain, wavelet coefficients, and autoregression have been studied as well as electrode reduction and frequency interval reduction. (4) First, the features/properties (algorithms) are reduced, the electrodes are reduced, and the frequency range is reduced, which is followed by the construction of new signals based on the proposed formulas. Then, a Common Spatial Pattern is used to remove noise and feature extraction and is classified by a classifier. (5) A separate study with a deep sampling method has been implemented as feature selection in several layers with functions and different window sizes. This part is also associated with reducing the feature and reducing the frequency range. All items expressed in data set IIa from BCI competition IV (the left hand and right hand) have been evaluated between one and three channels, with better results for similar cases (in close proximity). Our method demonstrated an increased accuracy by 5 to 8% and an increased kappa by 5%

    Towards simultaneous electroencephalography and functional near-infrared spectroscopy for improving diagnostic accuracy in prolonged disorders of consciousness: a healthy cohort study

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    Qualitative clinical assessments of the recovery of awareness after severe brain injury require an assessor to differentiate purposeful behaviour from spontaneous behaviour. As many such behaviours are minimal and inconsistent, behavioural assessments are susceptible to diagnostic errors. Advanced neuroimaging tools such as functional magnetic resonance imaging and electroencephalography (EEG) can bypass behavioural responsiveness and reveal evidence of covert awareness and cognition within the brains of some patients, thus providing a means for more accurate diagnoses, more accurate prognoses, and, in some instances, facilitated communication. As each individual neuroimaging method has its own advantages and disadvantages (e.g., signal resolution, accessibility, etc.), this thesis studies on healthy individuals a burgeoning technique of non-invasive electrical and optical neuroimaging—simultaneous EEG and functional near-infrared spectroscopy (fNIRS)—that can be applied at the bedside. Measuring reliable covert behaviours is correlated with participant engagement, instrumental sensitivity and the accurate localisation of responses, aspects which are further addressed over three studies. Experiment 1 quantifies the typical EEG changes in response to covert commands in the absence and presence of an object. This is investigated to determine whether a goal-directed task can yield greater EEG control accuracy over simple monotonous imagined single-joint actions. Experiment 2 characterises frequency domain NIRS changes in response to overt and covert hand movements. A method for reconstructing haemodynamics using the less frequently investigated phase parameter is outlined and the impact of noise contaminated NIRS measurements are discussed. Furthermore, classification performances between frequency-domain and continuous-wave-like signals are compared. Experiment 3 lastly applies these techniques to determine the potential of simultaneous EEG-fNIRS classification. Here a sparse channel montage that would ultimately favour clinical utility is used to demonstrate whether such a hybrid method containing rich spatial and temporal information can improve the classification of covert responses in comparison to unimodal classification of signals. The findings and discussions presented within this thesis identify a direction for future research in order to more accurately translate the brain state of patients with a prolonged disorder of consciousness
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