Effective extraction and filtering of frequency components in physiological signals using sum-of-sinusoids modelling

In biological signal processing, modelling and extraction of specific frequency components constitute an important procedure for filtering signal components of interest as well as artefact removal. Under some interference scenarios, a satisfactory elimination of artefacts from the signal must be even performed by subtraction of an artefact waveform model or template, rather than the use of linear band-pass filters. That is the case of the gradient artefact induced in the EEG within the fMRI scanner, which cannot be characterized by a specific bandwidth or spectral content. This paper presents a simple and accurate approach based upon sum-of-sinusoids modelling for signal and artefact frequency components representation in physiological signals. According to the proposed method, each signal frequency component is approximated as a sinusoid, whose amplitude and phase parameters are estimated by making use of the Discrete Fourier Transform (DFT). The proposed approach reveals to perform an effective modelling and extraction of ECG signal components as well as underlying gradient artefacts in the EEG signal

Gradient artefact correction in the EEG signal recorded within the fMRI scanner

In recent years, combined EEG-fMRI has become a powerful brain imaging technique which is largely employed in clinical and neuroscience research. Parallel to the achievements reached in this area, a number of challenges remain to be overcome in order to consolidate such technique as an independent and effective method for brain imaging. In particular, the occurrence of gradient artefacts in the EEG signal due to the magnetic field of the fMRI magnetic scanner. This paper presents a proposal for modelling the variability of the gradient artefact template which makes use of the standard deviation and the slope differentiator between consecutive samples of the signals. Combination of such a model with the average artefact subtraction method achieves a reasonable elimination of the gradient artefact from EEG recordings

Electroencephalogram spindle detection from simultaneous Electroencephalogram (EEG) / functional Magnetic Resonance Imaging (fMRI) data for a sleep deprivation study

Sleep deprivation is an important and common problem with many consequences for mental and physical health. A sleep deprivation study was conducted to better understand the effects of insufficient sleep on cognitive functions and recovery. Studying specific spontaneous sleep waves occurring during the recovery sleep after sleep deprivation allows us to better understand the brain mechanism during sleep and its importance. The focus of my Master project is the detection of these sleep waves on scalp Electro-EncephaloGraphy (EEG) data, especially spindles, using different automatic detection algorithms and comparing them to a visual detection. Automated detection of these typical EEG discharges is challenging because many artifacts occur when EEG data are recorded within the Magnetic Resonance Imaging (MRI) scanner, so in presence of a large magnetic field. EEG data should be carefully processed from MRI related artifacts before considering detection of sleep specific discharges. In the second section of the thesis, we investigated within the whole brain the Blood Oxygen Level Dependent (BOLD) responses, measured with functional MRI, to identify brain areas involved in the generation of sleep spindles detected from scalp EEG. Preliminary results showed that automated detection was not accurate enough, because of residual artifact and other consequences of preprocessing. Moreover, visual detection is limited by the complexity of the EEG data. Therefore, there are expected improvements in post-review of the automated detection or investigating the optimal parameters for automated methods. The first BOLD response maps showed similarity between automated and visual detection and also with a study conducted by (Manuel Schabus et al. 2007). To conclude, further investigation on automated methods must be performed to find the best compromise between visual detection, automated detection, and post-review of automated detection