3 research outputs found
A systematic review on artifact removal and classification techniques for enhanced MEG-based BCI systems
Neurological disease victims may be completely paralyzed and unable to move, but they may still be able to think. Their brain activity is the only means by which they can interact with their environment. Brain-Computer Interface (BCI) research attempts to create tools that support subjects with disabilities. Furthermore, BCI research has expanded rapidly over the past few decades as a result of the interest in creating a new kind of human-to-machine communication. As magnetoencephalography (MEG) has superior spatial and temporal resolution than other approaches, it is being utilized to measure brain activity non-invasively. The recorded signal includes signals related to brain activity as well as noise and artifacts from numerous sources. MEG can have a low signal-to-noise ratio because the magnetic fields generated by cortical activity are small compared to other artifacts and noise. By using the right techniques for noise and artifact detection and removal, the signal-to-noise ratio can be increased. This article analyses various methods for removing artifacts as well as classification strategies. Additionally, this offers a study of the influence of Deep Learning models on the BCI system. Furthermore, the various challenges in collecting and analyzing MEG signals as well as possible study fields in MEG-based BCI are examined
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ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals
Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository
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Development of machine learning-based open-source tools for processing and analysis of extracellular neuronal signals to facilitate disease monitoring
Neuronal signals are recordings of the electrical activity of the brain, which allow gaining insight into a diverse range of information. Like other physiological signals, extensive processing and analysis must be carried out in order to extract useful information. In this context, the neuroscience community has developed different open-access tools and pipelines for the different steps involved to facilitate the studies and make more advancements in the field. The aim of the research reported in this thesis is the development of tools and pipelines to facilitate the use of machine learning techniques in chronically recorded invasive signals for early disease detection. This includes the selection of the state-of-the-art for artefact detection and removal, the processing of the signal to feed the models, and lastly a robust machine learning based classifier. The main contributions of this thesis to the application of machine learning in neuronal signal processing include an open-access tool for benchmarking the performance of artefact detection and removal with ML with over 120 articles, the creation of a toolbox with novel methods to detect and remove artefacts from extracellular neuronal signals recorded in the form of local field potentials, a novel channel independent artefact removal method based on the forecasting of normal activity to replace affected segments, an innovative ML pipeline to detect and classify brain states from the processed local field potentials, and lastly finding novel biomarkers from these models and properly assess them against the existing literature