neural decoding widely exploits machine learning for classifying electroencephalographic (EEG) signals for brain-computer interface applications. Recent advancements in neural decoding regards the use of brain functio...
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ISBN:
(纸本)9783031716010;9783031716027
neural decoding widely exploits machine learning for classifying electroencephalographic (EEG) signals for brain-computer interface applications. Recent advancements in neural decoding regards the use of brain functional connectivity estimates as input features and the adoption of convolutional neuralnetworks (CNNs) to realize decoders. Moreover, explainable artificial intelligence (XAI) approaches based on CNNs are growing interest in the neuroscience community, for validating the knowledge learned by networks and for using the decoder not only to classify the EEG but also to analyze it in a data-driven way, without a priori assumptions. However, the adoption of connectivity estimates for neural decoding is still in its infancy, as adopts non-directed connectivity measures, limits the analysis of few interactions/frequency ranges, and exploits classic machine learning approaches without exploring CNNs. Moreover, XAI approaches have never been applied to analyze EEG-based functional connectivity. To overcome these limitations, we design and apply a CNN for processing directed connectivity measures estimated via spectral Granger causality. the CNN automatically learns features in the frequency and spatial domains, and it is coupled with an explanation technique (DeepLIFT) for highlighting the most relevant connectivity inflow and outflow associated to each decoded brain state. Our approach is applied to motor imagery decoding, and achieves state-of-the-art performance compared to existing networks. DeepLIFT relevance representations match the directional interactions known occurring when imagining movements, validating the features related to the brain network, as learned by the CNN.
Convolutional neuralnetworks (CNNs) have revolutionized motor decoding from electroencephalographic (EEG) signals, showcasing their ability to outperform traditional machine learning, especially for Brain-Computer In...
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ISBN:
(纸本)9783031716010;9783031716027
Convolutional neuralnetworks (CNNs) have revolutionized motor decoding from electroencephalographic (EEG) signals, showcasing their ability to outperform traditional machine learning, especially for Brain-Computer Interface (BCI) applications. By processing also other recording modalities (e.g., electromyography, EMG) together with EEG signals, motor decoding improved. However, multi-modal algorithms for decoding hand movements are mainly applied to simple movements (e.g., wrist flexion/extension), while their adoption for decoding complex movements (e.g., different grip types) is still under-investigated. In this study, we recorded EEG and EMG signals from 12 participants while they performed a delayed reach-to-grasping task towards one out of four possible objects (a handle, a pin, a card, and a ball), and we addressed multi-modal EEG+EMG decoding with a dual-branch CNN. Each branch of the CNN was based on EEGNet. the performance of the multi-modal approach was compared to mono-modal baselines (based on EEG or EMG only). the multi-modal EEG+EMG pipeline outperformed the EEG-based pipeline during movement initiation, while it outperformed the EMG-based pipeline in motor preparation. Finally, the multi-modal approach was capable of accurately discriminating between grip types widely during the task, especially from movement initiation. Our results further validate multi-modal decoding for potential future BCI applications, aiming at achieving a more natural user experience.
Primary hyperparathyroidism (pHPT) is a prevalent endocrine disorder characterized by excessive production of parathyroid hormone due to hyperactive parathyroid glands. this paper aims to enhance the diagnostic accura...
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ISBN:
(纸本)9783031716010;9783031716027
Primary hyperparathyroidism (pHPT) is a prevalent endocrine disorder characterized by excessive production of parathyroid hormone due to hyperactive parathyroid glands. this paper aims to enhance the diagnostic accuracy of PET/CT using F-18-fluorocholine (FCH) by employing radiomic image analysis to differentiate hyperfunctioning parathyroid glands (HPTG) from thyroid gland (TG) normal or adenomatous tissue. To this aim, the paper contributes a novel, real-life dataset (made publicly available online for download) along with several benchmarks. First, we collected and labeled FCH PET/CT images from 92 patients with pHPT, extracting 56 s-order and higher-order radiomic features using the software LIFEx. these features were analyzed in 98 HPTG and 91 TG findings, comparing clinical characteristics via nonparametric Wilcoxon rank sum tests. then, several variants of patternrecognition models (k-nearest neighbor and random forest) and deep neuralnetworks were applied to the task of discriminating between HPTG and TG over the dataset in order to fix baseline results for the Community to challenge. Moreover, two ensemble methods are proposed that combine the aforementioned classifiers, achieving an area under the curve of up to 92.30%. In conclusion, the present integration of radiomic features and machine learning provides a promising approach to the task, setting a benchmark for future research in the field.
In order to ensure high productivity and quality in industrial production, early identification of tool wear is needed. Within the context of Industry 4.0, we integrate wear monitoring of solid carbide milling and dri...
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Multi-instance multi-label learning (MIML) is a framework in machine learning in which each object is represented by multiple instances and associated with multiple labels. this relatively new approach has achieved su...
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In this work, we analyse different temporal feature extraction window approaches, in combination with short-time heat and electric pain stimuli. thereby, we focus on the physiological signals of the Experimentally Ind...
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Pain intensity recognition still constitutes a challenging classification task. In this work, we focus on the physiological signals of the publicly available BioVid Heat Pain Database, which was collected at Ulm Unive...
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