Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification

Objective. The main goal of this work is to develop a model for multi-sensor signals such as MEG or EEG signals, that accounts for the inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI type experiments. Approach. The method involves linear mixed effects statistical model, wavelet transform and spatial filtering, and aims at the characterization of localized discriminant features in multi-sensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e. discriminant) and background noise, using a very simple Gaussian linear mixed model. Main results. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data, in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. Significance. The combination of linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves on earlier results on similar problems, and the three main ingredients all play an important role

Noise Reduction of EEG Signals Using Autoencoders Built Upon GRU based RNN Layers

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an efficient noise reduction technique to get more accurate recordings. Numerous traditional techniques such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), wavelet transformations and machine learning techniques were proposed for reducing the noise in EEG signals. The aim of this paper is to investigate the effectiveness of stacked autoencoders built upon Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) layers (GRU-AE) against PCA. To achieve this, Harrell-Davis decile values for the reconstructed signals’ signal-to- noise ratio distributions were compared and it was found that the GRU-AE outperformed PCA for noise reduction of EEG signals

Topography-Time-Frequency Atomic Decomposition for Event-Related M/EEG Signals.

International audienceWe present a method for decomposing MEG or EEG data (channel x time x trials) into a set of atoms with fixed spatial and time-frequency signatures. The spatial part (i.e., topography) is obtained by independent component analysis (ICA). We propose a frequency prewhitening procedure as a pre-processing step before ICA, which gives access to high frequency activity. The time-frequency part is obtained with a novel iterative procedure, which is an extension of the matching pursuit procedure. The method is evaluated on a simulated dataset presenting both low-frequency evoked potentials and high-frequency oscillatory activity. We show that the method is able to recover well both low-frequency and high-frequency simulated activities. There was however cross-talk across some recovered components due to the correlation introduced in the simulation

Stacked Convolutional Recurrent Auto-encoder for Noise Reduction in EEG

Electroencephalogram (EEG) can be used to record electrical potentials in the brain by attaching electrodes to the scalp. However, these low amplitude recordings are susceptible to noise which originates from several sources including ocular, pulse and muscle artefacts. Their presence has a severe impact on analysis and diagnoses of brain abnormalities. This research assessed the effectiveness of a stacked convolutional-recurrent auto-encoder (CR-AE) for noise reduction of EEG signal. Performance was evaluated using the signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR) in comparison to principal component analysis (PCA), independent component analysis (ICA) and a simple auto-encoder (AE). The Harrell-Davis quantile estimator was used to compare SNR and PSNR distributions of reconstructed and raw signals. It was found that the proposed CR-AE achieved a mean SNR of 5:53 db and signicantly increased the SNR across all quantiles for each channel compared to the state-of-the-art methods. However, though SNR increased PSNR did not and the proposed CR-AE was outperformed by each baseline across the majority of quantiles for all channels. In addition, though reconstruction error was very low none of the proposed CR-AE architectures could generalize to the second dataset

MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG.

NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions

Two perspectives on response monitoring: Perfectionism-related variations and post-response adaptation

The cognitive system needs to monitor actions to ensure that the intended actions are successfully executed and to intervene when deviations from the intended actions are detected. In two studies, we investigated systematic variations of response monitoring between (Study 1) and within (Study 2) individuals. We assessed response monitoring using electrophysiological markers. The error/correct negativity (Ne/c) and the error/correct positivity (Pe/c) are both components of the event-related potential that occur within 300 ms after a motor response. Usually, they have higher peak amplitudes following errors compared to correct responses. In Study 1, we related these indicators of response monitoring to two dimensions of perfectionism and found that individuals who strive for flawlessness purely because they are afraid of being evaluated negatively by others (evaluative concern perfectionists) displayed less error-specific early response monitoring (indicated by the Ne/c) than non-perfectionists and individuals who set themselves high goals and are internally motivated to perform flawlessly (personal standards perfectionists). In Study 2, we linked the single-trial peak estimates of the Ne/c and Pe/c amplitudes to indicators of post-response adaptation derived by a diffusion model decompo¬sition of post-response times and accuracies. We found that early response monitoring reflected by the Ne/c was associated with a higher decision threshold and a greater focus on task-relevant features on the subsequent trial. The Pe/c, on the other hand, was associated with a lower decision threshold when speed was relevant. The interplay of Ne/c- and Pe/c-related processes may thus ensure that subsequent responses are as fast and as accurate as possible by adjusting the decision threshold. The thesis points out how future research could benefit from integrating both levels of response monitoring by investigating how individual differences as described in Study 1 modulate basic post-response adaptation mechanisms as delineated in Study 2

Spatio-Temporal Approaches to Denoising and Feature Extraction in Rapid Image Triage

Ph.DDOCTOR OF PHILOSOPH

A denoising algorithm for surface EMG decomposition

The goal of the present thesis was to investigate a novel motor unit potential train (MUPT) editing routine, based on decreasing the variability in shape (variance ratio, VR) of the MUP ensemble. Decomposed sEMG data from 20 participants at 60% MVC of wrist flexion was used. There were two levels of denoising (relaxed and strict) criteria for removing discharge times associated with waveforms that did not decrease the VR and increase its signal-to-noise ratio (SNR) of the MUP ensemble. The peak-to-peak amplitude and the duration between the positive and negative peaks for the MUP template were dependent on the level of denoising (p’s 0.05). The same was true between denoising criteria (p>0.05). Editing the MUPT based on MUP shape resulted in significant differences in measures extracted from the MUP template, with trivial difference between the standard error of estimate for mean IDIs between the complete and denoised MUPTs