43 results on '"Ieracitano, Cosimo"'
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2. An explainable embedded neural system for on-board ship detection from optical satellite imagery
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Ieracitano, Cosimo, Mammone, Nadia, Spagnolo, Fanny, Frustaci, Fabio, Perri, Stefania, Corsonello, Pasquale, and Morabito, Francesco C.
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- 2024
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3. A Deep Cognitive Venetian Blinds System for Automatic Estimation of Slat Orientation
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Ieracitano, Cosimo, Nicoletti, Francesco, Arcuri, Natale, Ruggeri, Giuseppe, Versaci, Mario, Morabito, Francesco Carlo, and Mammone, Nadia
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- 2022
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4. A novel explainable machine learning approach for EEG-based brain-computer interface systems
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Ieracitano, Cosimo, Mammone, Nadia, Hussain, Amir, and Morabito, Francesco Carlo
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- 2022
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5. A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images
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Ieracitano, Cosimo, Mammone, Nadia, Versaci, Mario, Varone, Giuseppe, Ali, Abder-Rahman, Armentano, Antonio, Calabrese, Grazia, Ferrarelli, Anna, Turano, Lorena, Tebala, Carmela, Hussain, Zain, Sheikh, Zakariya, Sheikh, Aziz, Sceni, Giuseppe, Hussain, Amir, and Morabito, Francesco Carlo
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- 2022
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6. Editorial Topical Collection: "Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment".
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Ieracitano, Cosimo and Zhang, Xuejun
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LANGUAGE models , *GREY Wolf Optimizer algorithm , *MACULAR degeneration , *SUPERVISED learning , *CROHN'S disease , *IMAGE segmentation , *KNEE - Abstract
This document is an editorial introducing a topical collection titled "Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment." The collection focuses on the integration of biomedical imaging techniques with advanced data analytics in healthcare. The editorial highlights the potential of this convergence to aid clinicians in disease diagnosis and treatment, emphasizing the importance of timely and accurate diagnoses. The collection includes thirteen papers that cover various advancements in biomedical imaging and data analytics, such as label-free imaging technologies, speech enhancement models, deep learning-based diagnostic tools, and automated segmentation methods. These contributions demonstrate the potential of artificial intelligence in analyzing complex imaging data and improving diagnostic accuracy and treatment efficacy. [Extracted from the article]
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- 2024
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7. A novel statistical analysis and autoencoder driven intelligent intrusion detection approach
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Ieracitano, Cosimo, Adeel, Ahsan, Morabito, Francesco Carlo, and Hussain, Amir
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- 2020
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8. A deep CNN approach to decode motor preparation of upper limbs from time–frequency maps of EEG signals at source level
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Mammone, Nadia, Ieracitano, Cosimo, and Morabito, Francesco C.
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- 2020
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9. A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia
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Ieracitano, Cosimo, Mammone, Nadia, Hussain, Amir, and Morabito, Francesco C.
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- 2020
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10. A Few-Shot Transfer Learning Approach for Motion Intention Decoding from Electroencephalographic Signals.
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Mammone, Nadia, Ieracitano, Cosimo, Spataro, Rossella, Guger, Christoph, Cho, Woosang, and Morabito, Francesco Carlo
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ELECTROENCEPHALOGRAPHY , *SIGNAL processing , *MOTOR imagery (Cognition) , *MOTION , *CONVOLUTIONAL neural networks , *INTENTION - Abstract
In this study, a few-shot transfer learning approach was introduced to decode movement intention from electroencephalographic (EEG) signals, allowing to recognize new tasks with minimal adaptation. To this end, a dataset of EEG signals recorded during the preparation of complex sub-movements was created from a publicly available data collection. The dataset was divided into two parts: the source domain dataset (including 5 classes) and the support (target domain) dataset, (including 2 classes) with no overlap between the two datasets in terms of classes. The proposed methodology consists in projecting EEG signals into the space-frequency-time domain, in processing such projections (rearranged in channels × frequency frames) by means of a custom EEG-based deep neural network (denoted as EEGframeNET5), and then adapting the system to recognize new tasks through a few-shot transfer learning approach. The proposed method achieved an average accuracy of 72.45 ± 4.19% in the 5-way classification of samples from the source domain dataset, outperforming comparable studies in the literature. In the second phase of the study, a few-shot transfer learning approach was proposed to adapt the neural system and make it able to recognize new tasks in the support dataset. The results demonstrated the system's ability to adapt and recognize new tasks with an average accuracy of 80 ± 0.12% in discriminating hand opening/closing preparation and outperforming reported results in the literature. This study suggests the effectiveness of EEG in capturing information related to the motor preparation of complex movements, potentially paving the way for BCI systems based on motion planning decoding. The proposed methodology could be straightforwardly extended to advanced EEG signal processing in other scenarios, such as motor imagery or neural disorder classification. [ABSTRACT FROM AUTHOR]
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- 2024
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11. A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect
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Guellil, Imane, Adeel, Ahsan, Azouaou, Faical, Benali, Fodil, Hachani, Ala-Eddine, Dashtipour, Kia, Gogate, Mandar, Ieracitano, Cosimo, Kashani, Reza, and Hussain, Amir
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- 2021
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12. Micropumps for drug delivery systems: a new semi-linear elliptic boundary-value problem
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Versaci, Mario, Mammone, Nadia, Ieracitano, Cosimo, and Morabito, Francesco Carlo
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- 2021
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13. 12 - Explainable deep learning to information extraction in diagnostics and electrophysiological multivariate time series
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Morabito, Francesco Carlo, Campolo, Maurizio, Ieracitano, Cosimo, and Mammone, Nadia
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- 2024
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14. Editorial Topical Collection: "Explainable and Augmented Machine Learning for Biosignals and Biomedical Images".
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Ieracitano, Cosimo, Mahmud, Mufti, Doborjeh, Maryam, and Lay-Ekuakille, Aimé
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MACHINE learning , *ARTIFICIAL intelligence , *COLLECTIONS - Abstract
This document is an editorial introducing a topical collection on "Explainable and Augmented Machine Learning for Biosignals and Biomedical Images." It discusses the challenges of machine learning (ML) algorithms in the biomedical field due to their lack of transparency and the emergence of explainable artificial intelligence (xAI) techniques to address this issue. The collection includes ten papers that explore the latest advancements in xAI applied to various medical applications, such as brain function analysis, cancer biomarker identification, stress detection, emotion classification, and disease diagnosis. The papers present innovative computational methods that have the potential to be deployed in clinical contexts. [Extracted from the article]
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- 2023
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15. A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting.
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Varone, Giuseppe, Ieracitano, Cosimo, Çiftçioğlu, Aybike Özyüksel, Hussain, Tassadaq, Gogate, Mandar, Dashtipour, Kia, Al-Tamimi, Bassam Naji, Almoamari, Hani, Akkurt, Iskender, and Hussain, Amir
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ATTENUATION coefficients , *MULTILAYER perceptrons , *MASS attenuation coefficients , *STANDARD deviations , *MACHINE learning - Abstract
The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and γ -rays in industrial and healthcare facilities. Heavy materials' shielding characteristics hold a lot of potential for bolstering concrete chunks. The mass attenuation coefficient is the main physical factor that is utilized to measure the narrow beam γ -ray attenuation of various combinations of magnetite and mineral powders with concrete. Data-driven machine learning approaches can be investigated to assess the gamma-ray shielding behavior of composites as an alternative to theoretical calculations, which are often time- and resource-intensive during workbench testing. We developed a dataset using magnetite and seventeen mineral powder combinations at different densities and water/cement ratios, exposed to photon energy ranging from 1 to 100 6 kiloelectronvolt (KeV). The National Institute of Standards and Technology (NIST) photon cross-section database and software methodology (XCOM) was used to compute the concrete's γ -ray shielding characteristics (LAC). The XCOM-calculated LACs and seventeen mineral powders were exploited using a range of machine learning (ML) regressors. The goal was to investigate whether the available dataset and XCOM-simulated LAC can be replicated using ML techniques in a data-driven approach. The minimum absolute error (MAE), root mean square error (RMSE), and R 2 s c o r e were employed to assess the performance of our proposed ML models, specifically a support vector machine (SVM), 1d-convolutional neural network (CNN), multi-Layer perceptrons (MLP), linear regressor, decision tree, hierarchical extreme machine learning (HELM), extreme learning machine (ELM), and random forest networks. Comparative results showed that our proposed HELM architecture outperformed state-of-the-art SVM, decision tree, polynomial regressor, random forest, MLP, CNN, and conventional ELM models. Stepwise regression and correlation analysis were further used to evaluate the forecasting capability of ML techniques compared to the benchmark XCOM approach. According to the statistical analysis, the HELM model showed strong consistency between XCOM and predicted LAC values. Additionally, the HELM model performed better in terms of accuracy than the other models used in this study, yielding the highest R2score and the lowest MAE and RMSE. [ABSTRACT FROM AUTHOR]
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- 2023
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16. An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing.
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Morabito, Francesco Carlo, Ieracitano, Cosimo, and Mammone, Nadia
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- 2023
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17. A Conditional Generative Adversarial Network and Transfer Learning-Oriented Anomaly Classification System for Electrospun Nanofibers.
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Ieracitano, Cosimo, Mammone, Nadia, Paviglianiti, Annunziata, and Morabito, Francesco Carlo
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GENERATIVE adversarial networks , *NANOFIBERS , *CONVOLUTIONAL neural networks , *SCANNING electron microscopes , *SCANNING electron microscopy , *SCANNING systems - Abstract
This paper proposes a generative model and transfer learning powered system for classification of Scanning Electron Microscope (SEM) images of defective nanofibers (D-NF) and nondefective nanofibers (ND-NF) produced by electrospinning (ES) process. Specifically, a conditional-Generative Adversarial Network (c-GAN) is developed to generate synthetic D-NF/ND-NF SEM images. A transfer learning-oriented strategy is also proposed. First, a Convolutional Neural Network (CNN) is pre-trained on real images. The transfer-learned CNN is trained on synthetic SEM images and validated on real ones, reporting accuracy rate up to 95.31%. The achieved encouraging results endorse the use of the proposed generative model in industrial applications as it could reduce the number of needed laboratory ES experiments that are costly and time consuming. [ABSTRACT FROM AUTHOR]
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- 2022
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18. Contributors
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Alippi, Cesare, Brown, David G., Campolo, Maurizio, Choe, Yoonsuck, Duffy, Nigel, Érdi, Péter, Fink, Dan, Francon, Olivier, Galdi, Paola, Gonzalez, Santiago, Gori, Marco, Grossberg, Stephen, Hodjat, Babak, Ieracitano, Cosimo, Kant, Mohak, Kasabov, Nikola Kirilov, Kim, Youngsik, Kozma, Robert, Levine, Daniel S., Li, Qinbo, Liang, Jason, Maggini, Marco, Mammone, Nadia, Meyerson, Elliot, Miikkulainen, Risto, Morabito, Francesco Carlo, Navruzyan, Arshak, Ormandy, Roman, Ozawa, Seiichi, Park, Dookun, Perin, Jose Krause, Raju, Bala, Rawal, Aditya, Samuelson, Frank W., Serra, Angela, Shahrzad, Hormoz, Tagliaferri, Roberto, Tiezzi, Matteo, Werbos, Paul J., and Widrow, Bernard
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- 2024
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19. A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect
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Guellil, Imane, Adeel, Ahsan, Azouaou, Faical, Benali, Fodil, Hachani, Ala-Eddine, Dashtipour, Kia, Gogate, Mandar, Ieracitano, Cosimo, Kashani, Reza, and Hussain, Amir
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ComputingMethodologies_PATTERNRECOGNITION ,Arabizi, Sentiment analysis, Arabic, Arabic dialect, Translation, Transliteration - Abstract
In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach consists of constructing and applying a set of deep learning algorithms to classify the sentiment of Arabic messages as positive or negative. It was applied on Facebook messages written in Modern Standard Arabic (MSA) as well as in Algerian dialect (DALG, which is a low resourced-dialect, spoken by more than 40 million people) with both scripts Arabic and Arabizi. To handle Arabizi, we consider both options: transliteration (largely used in the research literature for handling Arabizi) and translation (never used in the research literature for handling Arabizi). For highlighting the effectiveness of a semi-supervised approach, we carried out different experiments using both corpora for the training (i.e. the corpus constructed automatically and the one that was reviewed manually). The experiments were done on many test corpora dedicated to MSA/DALG, which were proposed and evaluated in the research literature. Both classifiers are used, shallow and deep learning classifiers such as Random Forest (RF), Logistic Regression(LR) Convolutional Neural Network (CNN) and Long short-term memory (LSTM). These classifiers are combined with word embedding models such as Word2vec and fastText that were used for sentiment classification. Experimental results (F1 score up to 95% for intrinsic experiments and up to 89% for extrinsic experiments) showed that the proposed system outperforms the existing state-of-the-art methodologies (the best improvement is up to 25%).
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- 2021
20. Chapter 11 - Deep Learning Approaches to Electrophysiological Multivariate Time-Series Analysis
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Morabito, Francesco Carlo, Campolo, Maurizio, Ieracitano, Cosimo, and Mammone, Nadia
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- 2019
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21. Editorial: Advances in brain-computer interface technologies for closed-loop neuromodulation.
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Yuchen Xu, Hubin Zhao, and Ieracitano, Cosimo
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BRAIN-computer interfaces ,NEUROMODULATION - Published
- 2023
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22. A Hybrid-Domain Deep Learning-Based BCI For Discriminating Hand Motion Planning From EEG Sources.
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Ieracitano, Cosimo, Morabito, Francesco Carlo, Hussain, Amir, and Mammone, Nadia
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ELECTROENCEPHALOGRAPHY , *DEEP learning , *CONVOLUTIONAL neural networks , *MOTOR cortex , *WAVELET transforms , *BEAMFORMING - Abstract
In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-frequency-domain, as part of a hybrid strategy, to discriminate the temporal windows (i.e. EEG epochs) preceding hand sub-movements (open/close) and the resting state. To this end, for each EEG epoch, the associated cortical source signals in the motor cortex and the corresponding time-frequency (TF) maps are estimated via beamforming and Continuous Wavelet Transform (CWT), respectively. Two Convolutional Neural Networks (CNNs) are designed: specifically, the first CNN is trained over a dataset of temporal (T) data (i.e. EEG sources), and is referred to as T-CNN; the second CNN is trained over a dataset of TF data (i.e. TF-maps of EEG sources), and is referred to as TF-CNN. Two sets of features denoted as T-features and TF-features, extracted from T-CNN and TF-CNN, respectively, are concatenated in a single features vector (denoted as TTF-features vector) which is used as input to a standard multi-layer perceptron for classification purposes. Experimental results show a significant performance improvement of our proposed hybrid-domain DL approach as compared to temporal-only and time-frequency-only-based benchmark approaches, achieving an average accuracy of 7 6. 2 1 ± 3. 7 7 %. [ABSTRACT FROM AUTHOR]
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- 2021
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23. Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection
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Ieracitano, Cosimo, Adeel, Ahsan, Gogate, Mandar, Dashtipour, Kia, Morabito, Francesco Carlo, Larijani, Hadi, Raza, Ali, and Hussain, Amir
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FOS: Computer and information sciences ,Computer Science - Cryptography and Security ,I.5.1 ,K.6.5 ,Cryptography and Security (cs.CR) ,I.2.1 - Abstract
Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS., To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018)
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- 2018
24. Exploiting Deep Learning for Persian Sentiment Analysis
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Dashtipour, Kia, Gogate, Mandar, Adeel, Ahsan, Ieracitano, Cosimo, Larijani, Hadi, and Hussain, Amir
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FOS: Computer and information sciences ,I.5.0 ,Computer Science - Computation and Language ,I.2.7 ,Computation and Language (cs.CL) - Abstract
The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject's sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as topic, product, movie, news etc. Deep learning has recently emerged as a powerful machine learning technique to tackle a growing demand of accurate sentiment analysis. However, limited work has been conducted to apply deep learning algorithms to languages other than English, such as Persian. In this work, two deep learning models (deep autoencoders and deep convolutional neural networks (CNNs)) are developed and applied to a novel Persian movie reviews dataset. The proposed deep learning models are analyzed and compared with the state-of-the-art shallow multilayer perceptron (MLP) based machine learning model. Simulation results demonstrate the enhanced performance of deep learning over state-of-the-art MLP., To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018)
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- 2018
25. A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications.
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Ju, Ziyi, Gun, Li, Hussain, Amir, Mahmud, Mufti, and Ieracitano, Cosimo
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BRAIN-computer interfaces ,ADAPTIVE filters ,ELECTRIC wheelchairs ,FEATURE extraction ,SUPPORT vector machines ,MACHINE learning - Abstract
In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive direction tracking filter (ADT) is developed to extract feature information along the direction of shadow boundaries. The proposed algorithm avoids extraction of features around all directions of pixels, which significantly improves the efficiency and accuracy of shadow features extraction. Higher-order statistics (HOS) features such as skewness and kurtosis in addition to other optical features are used as input to different Machine Learning (ML) based classifiers, specifically, a Multilayer Perceptron (MLP), Autoencoder (AE), 1D-Convolutional Neural Network (1D-CNN) and Support Vector Machine (SVM), to perform the shadow boundaries detection task. Comparative results demonstrate that the proposed MLP-based system outperforms all the other state-of-the-art approaches, reporting accuracy rates up to 84.63%. [ABSTRACT FROM AUTHOR]
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- 2020
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26. A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings.
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Ieracitano, Cosimo, Mammone, Nadia, Bramanti, Alessia, Hussain, Amir, and Morabito, Francesco C.
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NEURAL circuitry , *DEMENTIA , *ELECTROENCEPHALOGRAPHY , *MILD cognitive impairment , *ALZHEIMER'S disease - Abstract
Abstract A data-driven machine deep learning approach is proposed for differentiating subjects with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC), by only analyzing noninvasive scalp EEG recordings. The methodology here proposed consists of evaluating the power spectral density (PSD) of the 19-channels EEG traces and representing the related spectral profiles into 2- d gray scale images (PSD-images). A customized Convolutional Neural Network with one processing module of convolution, Rectified Linear Units (ReLu) and pooling layer (CNN 1) is designed to extract from PSD-images some suitable features and to perform the corresponding two and three-ways classification tasks. The resulting CNN is shown to provide better classification performance when compared to more conventional learning machines; indeed, it achieves an average accuracy of 89.8% in binary classification and of 83.3% in three-ways classification. These results encourage the use of deep processing systems (here, an engineered first stage, namely the PSD-image extraction, and a second or multiple CNN stage) in challenging clinical frameworks. [ABSTRACT FROM AUTHOR]
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- 2019
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27. Brain Network Analysis of Compressive Sensed High-Density EEG Signals in AD and MCI Subjects.
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Mammone, Nadia, De Salvo, Simona, Bonanno, Lilla, Ieracitano, Cosimo, Marino, Silvia, Marra, Angela, Bramanti, Alessia, and Morabito, Francesco C.
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Alzheimer's disease (AD) is a neurodegenerative disorder that causes a loss of connections between neurons. The goal of this paper is to construct a complex network model of the brain-electrical activity, using high-density EEG (HD-EEG) recordings, and to compare the network organization in AD, mild cognitive impaired (MCI), and healthy control (CNT) subjects. The HD-EEG of 16 AD, 16 MCI, and 12 CNT was recorded during an eye-closed resting state. The permutation disalignment index (PDI) was used to describe the dissimilarity between EEG signals and to construct the connection matrices of the network model. The three groups were found to have significantly different (p $ <$ 0.001) characteristic path length ($\lambda$), average clustering coefficient (CC), and the global efficiency (GE). This is the first time that HD-EEG signals of AD, MCI, and CNT have been compared and that PDI has been used to discriminate between the three groups. Considering the large amount of data originating from HD-EEG acquisition, compared to standard EEG, the aim of this paper is also to assess that compression did not alter the results of the complex network analysis. Compressive sensing was adopted to compress and reconstruct the HD-EEG signals with minimal information loss, achieving an average structural similarity index of 0.954 (AD), 0.957 (MCI), and 0.959 (CNT). When applied to the reconstructed HD-EEG, complex network analysis provided a substantially unaltered performance, compared to the analysis of the original signals: $\lambda$ , CC, and GE of the three groups were indeed still significantly different (p $ <$ 0.001). [ABSTRACT FROM AUTHOR]
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- 2019
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28. List of Contributors
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Alippi, Cesare, Brown, David G., Campolo, Maurizio, Choe, Yoonsuck, Duffy, Nigel, Érdi, Péter, Fink, Daniel, Francon, Olivier, Galdi, Paola, Grossberg, Stephen, Hodjat, Babak, Ieracitano, Cosimo, Kasabov, Nikola, Kim, Youngsik, Kozma, Robert, Levine, Daniel S., Liang, Jason, Mammone, Nadia, Meyerson, Elliot, Miikkulainen, Risto, Morabito, Francesco Carlo, Navruzyan, Arshak, Ormandy, Roman, Ozawa, Seiichi, Park, Dookun, Perin, Jose Krause, Raju, Bala, Rawal, Aditya, Samuelson, Frank W., Serra, Angela, Shahrzad, Hormoz, Szu, Harold, Tagliaferri, Roberto, The Al Working Group, Werbos, Paul J., and Widrow, Bernard
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- 2019
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29. Information Theoretic-Based Interpretation of a Deep Neural Network Approach in Diagnosing Psychogenic Non-Epileptic Seizures.
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Gasparini, Sara, Campolo, Maurizio, Ieracitano, Cosimo, Mammone, Nadia, Ferlazzo, Edoardo, Sueri, Chiara, Tripodi, Giovanbattista Gaspare, Aguglia, Umberto, and Morabito, Francesco Carlo
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ARTIFICIAL neural networks ,PSYCHOGENIC nonepileptic seizures ,SUPERVISED learning ,INFORMATION theory ,ELECTROENCEPHALOGRAPHY ,DIAGNOSIS - Abstract
The use of a deep neural network scheme is proposed to help clinicians solve a difficult diagnosis problem in neurology. The proposed multilayer architecture includes a feature engineering step (from time-frequency transformation), a double compressing stage trained by unsupervised learning, and a classification stage trained by supervised learning. After fine-tuning, the deep network is able to discriminate well the class of patients from controls with around 90% sensitivity and specificity. This deep model gives better classification performance than some other standard discriminative learning algorithms. As in clinical problems there is a need for explaining decisions, an effort has been carried out to qualitatively justify the classification results. The main novelty of this paper is indeed to give an entropic interpretation of how the deep scheme works and reach the final decision. [ABSTRACT FROM AUTHOR]
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- 2018
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30. Hierarchical clustering of the electroencephalogram spectral coherence to study the changes in brain connectivity in Alzheimer's disease.
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Mammone, Nadia, Bonanno, Lilla, De Salvo, Simona, Bramanti, Alessia, Bramanti, Placido, Adeli, Hojjat, Ieracitano, Cosimo, Campolo, Maurizio, and Morabito, Francesco C.
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- 2016
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31. Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings.
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Morabito, Francesco Carlo, Campolo, Maurizio, Ieracitano, Cosimo, Ebadi, Javad Mohammad, Bonanno, Lilla, Bramanti, Alessia, Desalvo, Simona, Mammone, Nadia, and Bramanti, Placido
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- 2016
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32. A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity.
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Mammone, Nadia, De Salvo, Simona, Ieracitano, Cosimo, Marino, Silvia, Marra, Angela, Corallo, Francesco, and Morabito, Francesco Carlo
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NEUROLOGICAL disorders ,ELECTROENCEPHALOGRAPHY ,ELECTRIC potential ,MILD cognitive impairment ,DEMENTIA - Abstract
In the study of neurological disorders, Electroencephalographic (EEG) signal processing can provide valuable information because abnormalities in the interaction between neuron circuits may reflect on macroscopic abnormalities in the electrical potentials that can be detected on the scalp. A Mild Cognitive Impairment (MCI) condition, when caused by a disorder degenerating into dementia, affects the brain connectivity. Motivated by the promising results achieved through the recently developed descriptor of coupling strength between EEG signals, the Permutation Disalignment Index (PDI), the present paper introduces a novel PDI-based complex network model to evaluate the longitudinal variations in brain-electrical connectivity. A group of 33 amnestic MCI subjects was enrolled and followed-up with over four months. The results were compared to MoCA (Montreal Cognitive Assessment) tests, which scores the cognitive abilities of the patient. A significant negative correlation could be observed between MoCA variation and the characteristic path length (λ) variation (r = -0.56, p = 0.0006), whereas a significant positive correlation could be observed between MoCA variation and the variation of clustering coefficient (CC, r = 0.58, p = 0.0004), global efficiency (GE, r = 0.57, p = 0.0005) and small worldness (SW, r = 0.57, p = 0.0005). Cognitive decline thus seems to reflect an underlying cortical "disconnection" phenomenon: worsened subjects indeed showed an increased l and decreased CC, GE and SW. The PDI-based connectivity model, proposed in the present work, could be a novel tool for the objective quantification of longitudinal brain-electrical connectivity changes in MCI subjects. [ABSTRACT FROM AUTHOR]
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- 2017
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33. Permutation Jaccard Distance-Based Hierarchical Clustering to Estimate EEG Network Density Modifications in MCI Subjects.
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Mammone, Nadia, Ieracitano, Cosimo, Adeli, Hojjat, Bramanti, Alessia, and Morabito, Francesco C.
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ALZHEIMER'S disease , *ELECTROENCEPHALOGRAPHY , *HIERARCHICAL clustering (Cluster analysis) - Abstract
In this paper, a novel electroencephalographic (EEG)-based method is introduced for the quantification of brain-electrical connectivity changes over a longitudinal evaluation of mild cognitive impaired (MCI) subjects. In the proposed method, a dissimilarity matrix is constructed by estimating the coupling strength between every pair of EEG signals, Hierarchical clustering is then applied to group the related electrodes according to the dissimilarity estimated on pairs of EEG recordings. Subsequently, the connectivity density of the electrodes network is calculated. The technique was tested over two different coupling strength descriptors: wavelet coherence (WC) and permutation Jaccard distance (PJD), a novel metric of coupling strength between time series introduced in this paper. Twenty-five MCI patients were enrolled within a follow-up program that consisted of two successive evaluations, at time T0 and at time T1, three months later. At T1, four subjects were diagnosed to have converted to Alzheimer’s Disease (AD). When applying the PJD-based method, the converted patients exhibited a significantly increased PJD ($p < 0.05$), i.e., a reduced overall coupling strength, specifically in delta and theta bands and in the overall range (0.5–32 Hz). In addition, in contrast to stable MCI patients, converted patients exhibited a network density reduction in every subband (delta, theta, alpha, and beta). When WC was used as coupling strength descriptor, the method resulted in a less sensitive and specific outcome. The proposed method, mixing nonlinear analysis to a machine learning approach, appears to provide an objective evaluation of the connectivity density modifications associated to the MCI-AD conversion, just processing noninvasive EEG signals. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
34. Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures.
- Author
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Lo Giudice, Michele, Varone, Giuseppe, Ieracitano, Cosimo, Mammone, Nadia, Tripodi, Giovanbattista Gaspare, Ferlazzo, Edoardo, Gasparini, Sara, Aguglia, Umberto, and Morabito, Francesco Carlo
- Subjects
PSYCHOGENIC nonepileptic seizures ,ARTIFICIAL neural networks ,CONVOLUTIONAL neural networks ,EPILEPSY ,FISHER discriminant analysis ,ELECTROENCEPHALOGRAPHY - Abstract
The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
35. A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls.
- Author
-
Varone, Giuseppe, Boulila, Wadii, Lo Giudice, Michele, Benjdira, Bilel, Mammone, Nadia, Ieracitano, Cosimo, Dashtipour, Kia, Neri, Sabrina, Gasparini, Sara, Morabito, Francesco Carlo, Hussain, Amir, and Aguglia, Umberto
- Subjects
PSYCHOGENIC nonepileptic seizures ,FUNCTIONAL connectivity ,MACHINE learning ,FISHER discriminant analysis ,LARGE-scale brain networks - Abstract
Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
36. An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals.
- Author
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Vargas-Lopez, Olivia, Perez-Ramirez, Carlos A., Valtierra-Rodriguez, Martin, Yanez-Borjas, Jesus J., Amezquita-Sanchez, Juan P., and Ieracitano, Cosimo
- Subjects
AUTOMOBILE drivers ,MACHINE learning ,USED cars ,SUPPORT vector machines ,ROOT-mean-squares ,EXTRATERRESTRIAL beings - Abstract
The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
37. A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling.
- Author
-
Farouq, Muhamed Wael, Boulila, Wadii, Hussain, Zain, Rashid, Asrar, Shah, Moiz, Hussain, Sajid, Ng, Nathan, Ng, Dominic, Hanif, Haris, Shaikh, Mohamad Guftar, Sheikh, Aziz, Hussain, Amir, Ieracitano, Cosimo, Mahmud, Mufti, Doborjeh, Maryam, and Lay-Ekuakille, Aimé
- Subjects
NON-small-cell lung carcinoma ,PARTIAL differential equations ,REGULATOR genes ,PHENOTYPES ,MACHINE learning ,GENE expression profiling - Abstract
Machine learning (ML)-based algorithms are playing an important role in cancer diagnosis and are increasingly being used to aid clinical decision-making. However, these commonly operate as 'black boxes' and it is unclear how decisions are derived. Recently, techniques have been applied to help us understand how specific ML models work and explain the rational for outputs. This study aims to determine why a given type of cancer has a certain phenotypic characteristic. Cancer results in cellular dysregulation and a thorough consideration of cancer regulators is required. This would increase our understanding of the nature of the disease and help discover more effective diagnostic, prognostic, and treatment methods for a variety of cancer types and stages. Our study proposes a novel explainable analysis of potential biomarkers denoting tumorigenesis in non-small cell lung cancer. A number of these biomarkers are known to appear following various treatment pathways. An enhanced analysis is enabled through a novel mathematical formulation for the regulators of mRNA, the regulators of ncRNA, and the coupled mRNA–ncRNA regulators. Temporal gene expression profiles are approximated in a two-dimensional spatial domain for the transition states before converging to the stationary state, using a system comprised of coupled-reaction partial differential equations. Simulation experiments demonstrate that the proposed mathematical gene-expression profile represents a best fit for the population abundance of these oncogenes. In future, our proposed solution can lead to the development of alternative interpretable approaches, through the application of ML models to discover unknown dynamics in gene regulatory systems. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
38. A novel explainable machine learning approach for EEG-based brain-computer interface systems.
- Author
-
Ieracitano, Cosimo, Mammone, Nadia, Hussain, Amir, and Morabito, Francesco Carlo
- Abstract
Electroencephalographic (EEG) recordings can be of great help in decoding the open/close hand’s motion preparation. To this end, cortical EEG source signals in the motor cortex (evaluated in the 1-s window preceding movement onset) are extracted by solving inverse problem through beamforming. EEG sources epochs are used as source-time maps input to a custom deep convolutional neural network (CNN) that is trained to perform 2-ways classification tasks: pre-hand close (HC) versus resting state (RE) and pre-hand open (HO) versus RE. The developed deep CNN works well (accuracy rates up to 89.65±5.29%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$89.65 \pm 5.29\%$$\end{document} for HC versus RE and 90.50±5.35%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$90.50 \pm 5.35\%$$\end{document} for HO versus RE), but the core of the present study was to explore the interpretability of the deep CNN to provide further insights into the activation mechanism of cortical sources during the preparation of hands’ sub-movements. Specifically,
occlusion sensitivity analysis was carried out to investigate which cortical areas are more relevant in the classification procedure. Experimental results show a recurrent trend of spatial cortical activation across subjects. In particular, the central region (close to the longitudinal fissure) and the right temporal zone of the premotor together with the primary motor cortex appear to be primarily involved. Such findings encourage an in-depth study of cortical areas that seem to play a key role in hand’s open/close preparation. [ABSTRACT FROM AUTHOR]- Published
- 2021
- Full Text
- View/download PDF
39. Compressibility of High-Density EEG Signals in Stroke Patients.
- Author
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Mammone, Nadia, De Salvo, Simona, Ieracitano, Cosimo, Marino, Silvia, Cartella, Emanuele, Bramanti, Alessia, Giorgianni, Roberto, and Morabito, Francesco C.
- Subjects
COMPRESSIBILITY ,HIGH pressure physics ,HIGH density storage ,ELECTROENCEPHALOGRAPHY ,BIOMEDICAL signal processing ,STROKE patients - Abstract
Stroke is a critical event that causes the disruption of neural connections. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. Intra- and extra-cellular currents are involved in the communication between neurons and the macroscopic effects of such currents can be detected at the scalp through electroencephalographic (EEG) sensors. EEG can be used to study the lesions in the brain indirectly, by studying their effects on the brain electrical activity. The primary goal of the present work was to investigate possible asymmetries in the activity of the two hemispheres, in the case one of them is affected by a lesion due to stroke. In particular, the compressibility of High-Density-EEG (HD-EEG) recorded at the two hemispheres was investigated since the presence of the lesion is expected to impact on the regularity of EEG signals. The secondary objective was to evaluate if standard low density EEG is able to provide such information. Eighteen patients with unilateral stroke were recruited and underwent HD-EEG recording. Each EEG signal was compressively sensed, using Block Sparse Bayesian Learning, at increasing compression rate. The two hemispheres showed significant differences in the compressibility of EEG. Signals acquired at the electrode locations of the affected hemisphere showed a better reconstruction quality, quantified by the Structural SIMilarity index (SSIM), than the EEG signals recorded at the healthy hemisphere (p < 0.05), for each compression rate value. The presence of the lesion seems to induce an increased regularity in the electrical activity of the brain, thus an increased compressibility. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
40. Editorial: Advances in brain-computer interface technologies for closed-loop neuromodulation.
- Author
-
Xu Y, Zhao H, and Ieracitano C
- Abstract
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
- Published
- 2023
- Full Text
- View/download PDF
41. AutoEncoder Filter Bank Common Spatial Patterns to Decode Motor Imagery From EEG.
- Author
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Mammone N, Ieracitano C, Adeli H, and Morabito FC
- Subjects
- Humans, Signal Processing, Computer-Assisted, Neural Networks, Computer, Electroencephalography methods, Imagination, Algorithms, Brain-Computer Interfaces
- Abstract
The present paper introduces a novel method, named AutoEncoder-Filter Bank Common Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography (EEG). AE-FBCSP is an extension of the well-established FBCSP and is based on a global (cross-subject) and subsequent transfer learning subject-specific (intra-subject) approach. A multi-way extension of AE-FBCSP is also introduced in this paper. Features are extracted from high-density EEG (64 electrodes), by means of FBCSP, and used to train a custom AE, in an unsupervised way, to project the features into a compressed latent space. Latent features are used to train a supervised classifier (feed forward neural network) to decode the imagined movement. The proposed method was tested using a public dataset of EEGs collected from 109 subjects. The dataset consists of right-hand, left-hand, both hands, both feet motor imagery and resting EEGs. AE-FBCSP was extensively tested in the 3-way classification (right hand vs left hand vs resting) and also in the 2-way, 4-way and 5-way ones, both in cross- and intra-subject analysis. AE-FBCSP outperformed standard FBCSP in a statistically significant way (p > 0.05) and achieved a subject-specific average accuracy of 89.09% in the 3-way classification. The proposed methodology performed subject-specific classification better than other comparable methods in the literature, applied to the same dataset, also in the 2-way, 4-way and 5-way tasks. One of the most interesting outcomes is that AE-FBCSP remarkably increased the number of subjects that responded with a very high accuracy, which is a fundamental requirement for BCI systems to be applied in practice.
- Published
- 2023
- Full Text
- View/download PDF
42. Guest Editorial: Advances in Deep Learning for Clinical and Healthcare Applications.
- Author
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Ieracitano C, Morabito FC, Squartini S, Huang K, Li X, and Mahmud M
- Published
- 2022
- Full Text
- View/download PDF
43. A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls.
- Author
-
Varone G, Boulila W, Lo Giudice M, Benjdira B, Mammone N, Ieracitano C, Dashtipour K, Neri S, Gasparini S, Morabito FC, Hussain A, and Aguglia U
- Subjects
- Cohort Studies, Humans, Machine Learning, Rest, Electroencephalography, Seizures
- Abstract
Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.
- Published
- 2021
- Full Text
- View/download PDF
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