CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction

Human electroencephalography (EEG) is a brain monitoring modality that senses cortical neuroelectrophysiological activity in high-temporal resolution. One of the greatest challenges posed in applications of EEG is the unstable signal quality susceptible to inevitable artifacts during recordings. To date, most existing techniques for EEG artifact removal and reconstruction are applicable to offline analysis solely, or require individualized training data to facilitate online reconstruction. We have proposed CLEEGN, a novel convolutional neural network for plug-and-play automatic EEG reconstruction. CLEEGN is based on a subject-independent pre-trained model using existing data and can operate on a new user without any further calibration. The performance of CLEEGN was validated using multiple evaluations including waveform observation, reconstruction error assessment, and decoding accuracy on well-studied labeled datasets. The results of simulated online validation suggest that, even without any calibration, CLEEGN can largely preserve inherent brain activity and outperforms leading online/offline artifact removal methods in the decoding accuracy of reconstructed EEG data. In addition, visualization of model parameters and latent features exhibit the model behavior and reveal explainable insights related to existing knowledge of neuroscience. We foresee pervasive applications of CLEEGN in prospective works of online plug-and-play EEG decoding and analysis

A Method for Optimizing the Artifact Subspace Reconstruction Performance in Low-Density EEG

— Electroencephalogram (EEG) plays a significant role in the analysis of cerebral activity, although the recorded electrical brain signals are always contaminated with artifacts. This represents the major issue limiting the use of EEG in daily life applications, as artifact removal process still remains a challenging task. Among the available methodologies, Artifact Subspace Reconstruction (ASR) is a promising tool that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameters have been validated only for high-density EEG acquisitions. In this regard, the present study proposes an enhanced procedure for the optimal individuation of ASR parameters, in order to successfully remove artifact in lowdensity EEG acquisitions (down to four channels). The proposed method starts from the analysis of real EEG data, to generate a large semi-simulated dataset with similar characteristics. Through a finetuning procedure on this semi-simulated data, the proposed method identifies the optimal parameters to be used for artifact removal on real data. The results show that the algorithm achieves an efficient removal of artifacts preserving brain signal information, also in low-density EEG signals, thus favoring the adoption of EEG also for more portable and/or daily-life applications

Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization

The significant advancements in electroencephalography (EEG)-driven technology have led to its widespread use in assessing stroke-related conditions. Over the years, various studies have explored the potential of EEG oscillatory patterns in neurological research, with several of them giving limited attention to the signal processing techniques employed, precluding a proper understanding of EEG oscillatory patterns under various conditions. To resolve this issue, we systematically investigated how artifacts impact EEG oscillatory rhythms associated with upper limb movement-related tasks. Thus, the EEG signals of motor tasks were acquired non-invasively from healthy subjects and processed using automated artifact-attenuation methods. Subsequently, the Mu and Beta bands in the brain's motor cortex region were extracted through time-frequency analysis and analyzed using relevant metrics. Experimental results revealed that artifacts in EEG would substantially influence the brain activation strength and response during motor tasks. Notably, signals preprocessed with Reduction of Electroencephalographic Artifacts based on Multi Wiener Filter and Enhanced Wavelet Independent Component Analysis (RELAX_MWF_wICA) showed better brain responses and high task classification performance compared to other methods and the raw signal across motor tasks. This study's findings revealed that the choice of signal processing technique is crucial, as it would influence its analysis and interpretation, thus highlighting the need for careful consideration and usage

Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods

Explainable deep learning solutions for the artifacts correction of EEG signals

L'attività celebrale può essere acquisita tramite elettroencefalografia (EEG) con degli elettrodi posti sullo scalpo del soggetto. Quando un segnale EEG viene acquisito si formano degli artefatti dovuti a: movimenti dei muscoli, movimenti degli occhi, attività del cuore o dovuti all'apparecchio di acquisizione stesso. Questi artefatti possono notevolmente compromettere la qualità dei segnali EEG. La rimozione di questi artefatti è fondamentale per molte discipline per ottenere un segnale pulito e poterlo utilizzare nel migli0re dei modi. Il machine learning (ML) è un esempio di tecnica che può essere utilizzata per classificare e rimuovere gli artefatti dai segnali EEG. Il deep learning (DL) è una branca del ML che è sviluppata ispirandosi all'architettura della corteccia cerebrale umana. Il DL è alla base della creazione dell'intelligenza artificiale e della costruzione di reti neurali (NN). Nella tesi applicheremo ICLabel che è una rete neurale che classifica le componenti indipendenti (IC), ottenute con la scomposizione tramite independent component analysis (ICA), in sette classi differenti: brain, eye, muscle, heart, channel noise, line noise e other. ICLabel calcola la probabilità che le ICs appartengano a ciascuna di queste sette classi. Durante questo lavoro di tesi abbiamo sviluppato una semplice rete neurale, simile a quella di ICLabel, che classifica le ICs in due classi: una contenente le ICs che corrispondono a quelli che sono i segnali base dell'attività cerebrale, l'altra invece contenente le ICs che non appartengono a questi segnali base. Abbiamo creato questa rete neurale per poter applicare poi un algoritmo di explainability (basato sulle reti neurali), chiamato GradCAM. Abbiamo, poi, comparato le performances di ICLabel e della rete neurale da noi sviluppata per vedere le differenze dal punto di vista della accuratezza e della precisione nella classificazione, come descritto nel capitolo. Abbiamo infine applicato GradCAM alla rete neurale da noi sviluppata per capire quali parti del segnale la rete usa per compiere le classificazioni, evidenziando sugli spettrogrammi delle ICs le parti più importanti del segnale. Possiamo dire poi, che come ci aspettavamo la CNN è guidata da componenti come quelle del line noise (che corrisponde alla frequenza di 50 Hz e armoniche più alte) per identificare le componenti non brain, mentre si concentra sul range da 1-30 Hz per identificare quelle brain. Anche se promettenti questi risultati vannno investigati. Inoltre GradCAM potrebbe essere applicato anche su ICLabel per spiegare la sua struttura più complessa.The brain electrical activity can be acquired via electroencephalography (EEG) with electrodes placed on the scalp of the individual. When EEG signals are recorded, signal artifacts such as muscular activities, blinking of eyes, and power line electrical noise can significantly affect the quality of the EEG signals. Machine learning (ML) techniques are an example of method used to classify and remove EEG artifacts. Deep learning is a type of ML inspired by the architecture of the cerebral cortex, that is formed by a dense network of neurons, simple processing units in our brain. In this thesis work we use ICLabel that is an artificial neural network developed by EEGLAB to automatically classify, that classifies the inidpendent component(ICs), obtained by the application of the independent component analysis (ICA), in seven classes, i.e., brain, eye, muscle, heart, channel noise, line noise, other. ICLabel provides the probability that each IC features belongs to one out of 6 artefact classes, or it is a pure brain component. We create a simple CNN similar to the ICLabel's one that classifies the EEG artifacts ICs in two classes, brain and not brain. and we added an explainability tool, i.e., GradCAM, to investigate how the algorithm is able to successfully classify the ICs. We compared the performances f our simple CNN versus those of ICLabel, finding that CNN is able to reach satisfactory accuracies (over two classes, i.e., brain/non-brain). Then we applied GradCAM to the CNN to understand what are the most important parts of the spectrogram that the network used to classify the data and we could speculate that, as expected, the CNN is driven by components such as the power line noise (50 Hz and higher harmonics) to identify non-brain components, while it focuses on the range 1-30 Hz to identify brain components. Although promising, these results need further investigations. Moreover, GradCAM could be later applied to ICLabel, too, in order to explain the more sophisticated DL model with 7 classes

A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling

Artifact Subspace Reconstruction (ASR) is an adaptive method for the online or offline correction of artifacts comprising multichannel electroencephalography (EEG) recordings. It repeatedly computes a principal component analysis (PCA) on covariance matrices to detect artifacts based on their statistical properties in the component subspace. We adapted the existing ASR implementation by using Riemannian geometry for covariance matrix processing. EEG data that were recorded on smartphone in both outdoors and indoors conditions were used for evaluation (N = 27). A direct comparison between the original ASR and Riemannian ASR (rASR) was conducted for three performance measures: reduction of eye-blinks (sensitivity), improvement of visual-evoked potentials (VEPs) (specificity), and computation time (efficiency). Compared to ASR, our rASR algorithm performed favorably on all three measures. We conclude that rASR is suitable for the offline and online correction of multichannel EEG data acquired in laboratory and in field conditions

Transformer Convolutional Neural Networks for Automated Artifact Detection in Scalp EEG

It is well known that electroencephalograms (EEGs) often contain artifacts due to muscle activity, eye blinks, and various other causes. Detecting such artifacts is an essential first step toward a correct interpretation of EEGs. Although much effort has been devoted to semi-automated and automated artifact detection in EEG, the problem of artifact detection remains challenging. In this paper, we propose a convolutional neural network (CNN) enhanced by transformers using belief matching (BM) loss for automated detection of five types of artifacts: chewing, electrode pop, eye movement, muscle, and shiver. Specifically, we apply these five detectors at individual EEG channels to distinguish artifacts from background EEG. Next, for each of these five types of artifacts, we combine the output of these channel-wise detectors to detect artifacts in multi-channel EEG segments. These segment-level classifiers can detect specific artifacts with a balanced accuracy (BAC) of 0.947, 0.735, 0.826, 0.857, and 0.655 for chewing, electrode pop, eye movement, muscle, and shiver artifacts, respectively. Finally, we combine the outputs of the five segment-level detectors to perform a combined binary classification (any artifact vs. background). The resulting detector achieves a sensitivity (SEN) of 60.4%, 51.8%, and 35.5%, at a specificity (SPE) of 95%, 97%, and 99%, respectively. This artifact detection module can reject artifact segments while only removing a small fraction of the background EEG, leading to a cleaner EEG for further analysis.Comment: This is an extension to a paper presented at the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Scottish Event Campus, Glasgow, UK, July 11-15, 202

ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS

Ph.DDOCTOR OF PHILOSOPH

Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA). We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications