Cosimo Ieracitano

2.8k total citations
42 papers, 1.7k citations indexed

About

Cosimo Ieracitano is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Cosimo Ieracitano has authored 42 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Cognitive Neuroscience, 9 papers in Artificial Intelligence and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Cosimo Ieracitano's work include EEG and Brain-Computer Interfaces (25 papers), Functional Brain Connectivity Studies (11 papers) and Neural dynamics and brain function (10 papers). Cosimo Ieracitano is often cited by papers focused on EEG and Brain-Computer Interfaces (25 papers), Functional Brain Connectivity Studies (11 papers) and Neural dynamics and brain function (10 papers). Cosimo Ieracitano collaborates with scholars based in Italy, United Kingdom and United States. Cosimo Ieracitano's co-authors include Francesco Carlo Morabito, Nadia Mammone, Amir Hussain, Alessia Bramanti, Ahsan Adeel, Maurizio Campolo, Simona De Salvo, Hojjat Adeli, Lilla Bonanno and Silvia Marino and has published in prestigious journals such as IEEE Access, Sensors and IEEE Transactions on Industrial Informatics.

In The Last Decade

Cosimo Ieracitano

38 papers receiving 1.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Cosimo Ieracitano Italy 19 873 440 212 210 194 42 1.7k
Enas Abdulhay Jordan 22 772 0.9× 316 0.7× 242 1.1× 284 1.4× 256 1.3× 49 2.1k
Jasmin Kevrić Bosnia and Herzegovina 14 824 0.9× 294 0.7× 528 2.5× 199 0.9× 170 0.9× 40 1.4k
Muhammad Tariq Sadiq China 28 1.2k 1.4× 445 1.0× 358 1.7× 333 1.6× 115 0.6× 67 2.3k
Gaoxiang Ouyang China 29 1.4k 1.7× 204 0.5× 356 1.7× 177 0.8× 88 0.5× 81 2.3k
Nidal Kamel Malaysia 22 1.2k 1.4× 206 0.5× 282 1.3× 330 1.6× 178 0.9× 183 2.4k
Syed Umar Amin Saudi Arabia 24 1.2k 1.4× 585 1.3× 360 1.7× 191 0.9× 508 2.6× 47 2.8k
Parisa Moridian Australia 17 745 0.9× 301 0.7× 154 0.7× 186 0.9× 36 0.2× 19 1.4k
Marjane Khodatars Iran 17 695 0.8× 302 0.7× 148 0.7× 171 0.8× 30 0.2× 19 1.4k
Chen Chen China 25 791 0.9× 208 0.5× 266 1.3× 174 0.8× 85 0.4× 186 1.9k
Mahboobeh Jafari Australia 16 625 0.7× 249 0.6× 116 0.5× 132 0.6× 35 0.2× 23 1.2k

Countries citing papers authored by Cosimo Ieracitano

Since Specialization
Citations

This map shows the geographic impact of Cosimo Ieracitano's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Cosimo Ieracitano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cosimo Ieracitano more than expected).

Fields of papers citing papers by Cosimo Ieracitano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cosimo Ieracitano. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Cosimo Ieracitano. The network helps show where Cosimo Ieracitano may publish in the future.

Co-authorship network of co-authors of Cosimo Ieracitano

This figure shows the co-authorship network connecting the top 25 collaborators of Cosimo Ieracitano. A scholar is included among the top collaborators of Cosimo Ieracitano based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Cosimo Ieracitano. Cosimo Ieracitano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ieracitano, Cosimo, et al.. (2025). An Explainable 3D-Deep Learning Model for EEG Decoding in Brain–Computer Interface Applications. International Journal of Neural Systems. 35(13). 2550073–2550073.
2.
Ieracitano, Cosimo, et al.. (2025). TIxAI: A Trustworthiness Index for eXplainable AI in skin lesions classification. Neurocomputing. 630. 129701–129701. 3 indexed citations
3.
4.
Abadal, Sergi, A. M. Mármol, Nadia Mammone, et al.. (2024). Graph neural networks for electroencephalogram analysis: Alzheimer’s disease and epilepsy use cases. Neural Networks. 181. 106792–106792. 11 indexed citations
5.
Morabito, Francesco Carlo, Cosimo Ieracitano, Nadia Mammone, et al.. (2024). Towards a Deep Learning Approach to Discriminate Hereditary Anemias. 339–344.
6.
Varone, Giuseppe, Cosimo Ieracitano, Aybike Özyüksel Çiftçioğlu, et al.. (2023). A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting. Entropy. 25(2). 253–253. 10 indexed citations
7.
Bevacqua, Martina T., Cosimo Ieracitano, Nadia Mammone, et al.. (2023). Electromagnetic Inverse Scattering via Deep Learning Enhanced by Virtual Experiments. 1231–1236.
8.
Mammone, Nadia, Cosimo Ieracitano, Hojjat Adeli, & Francesco Carlo Morabito. (2023). AutoEncoder Filter Bank Common Spatial Patterns to Decode Motor Imagery From EEG. IEEE Journal of Biomedical and Health Informatics. 27(5). 2365–2376. 46 indexed citations
9.
Ieracitano, Cosimo, Nadia Mammone, Mario Versaci, et al.. (2022). A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images. Neurocomputing. 481. 202–215. 102 indexed citations
10.
Mahmud, Mufti, Cosimo Ieracitano, M. Shamim Kaiser, Nadia Mammone, & Francesco Carlo Morabito. (2022). Applied Intelligence and Informatics. Communications in computer and information science. 4 indexed citations
11.
Ieracitano, Cosimo, Francesco Nicoletti, Natale Arcuri, et al.. (2022). A Deep Cognitive Venetian Blinds System for Automatic Estimation of Slat Orientation. Cognitive Computation. 14(6). 2203–2211. 4 indexed citations
12.
Varone, Giuseppe, Wadii Boulila, Bilel Benjdira, et al.. (2021). A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls. Sensors. 22(1). 129–129. 34 indexed citations
13.
Guellil, Imane, Ahsan Adeel, Faiçal Azouaou, et al.. (2021). A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect. SN Computer Science. 2(2). 28 indexed citations
14.
15.
Mammone, Nadia, Cosimo Ieracitano, & Francesco Carlo Morabito. (2020). A deep CNN approach to decode motor preparation of upper limbs from time–frequency maps of EEG signals at source level. Neural Networks. 124. 357–372. 115 indexed citations
16.
Varone, Giuseppe, Cosimo Ieracitano, Nadia Mammone, et al.. (2020). 1D Convolutional Neural Network approach to classify voluntary eye blinks in EEG signals for BCI applications. Iris (Roma Tre University). 1–7. 12 indexed citations
17.
Ieracitano, Cosimo, Nadia Mammone, Amir Hussain, & Francesco Carlo Morabito. (2019). A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Networks. 123. 176–190. 235 indexed citations
18.
Mammone, Nadia, Simona De Salvo, Lilla Bonanno, et al.. (2018). Brain Network Analysis of Compressive Sensed High-Density EEG Signals in AD and MCI Subjects. IEEE Transactions on Industrial Informatics. 15(1). 527–536. 80 indexed citations
19.
Mammone, Nadia, Cosimo Ieracitano, Hojjat Adeli, Alessia Bramanti, & Francesco Carlo Morabito. (2018). Permutation Jaccard Distance-Based Hierarchical Clustering to Estimate EEG Network Density Modifications in MCI Subjects. IEEE Transactions on Neural Networks and Learning Systems. 29(10). 5122–5135. 65 indexed citations
20.
Morabito, Francesco Carlo, Maurizio Campolo, Cosimo Ieracitano, et al.. (2016). Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings. 1–6. 115 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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