Fabio La Foresta

2.0k total citations · 1 hit paper
58 papers, 1.3k citations indexed

About

Fabio La Foresta is a scholar working on Cognitive Neuroscience, Signal Processing and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Fabio La Foresta has authored 58 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Cognitive Neuroscience, 17 papers in Signal Processing and 9 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Fabio La Foresta's work include EEG and Brain-Computer Interfaces (31 papers), Blind Source Separation Techniques (17 papers) and Neural dynamics and brain function (13 papers). Fabio La Foresta is often cited by papers focused on EEG and Brain-Computer Interfaces (31 papers), Blind Source Separation Techniques (17 papers) and Neural dynamics and brain function (13 papers). Fabio La Foresta collaborates with scholars based in Italy, Denmark and Montenegro. Fabio La Foresta's co-authors include Francesco Carlo Morabito, Nadia Mammone, Domenico Labate, Giuseppe Morabito, Isabella Palamara, B. Azzerboni, Alessia Bramanti, Mario Versaci, P. Vergallo and A. Lay-Ekuakille and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Applied Physics and Journal of Magnetism and Magnetic Materials.

In The Last Decade

Fabio La Foresta

50 papers receiving 1.3k citations

Hit Papers

Integration of LSTM and U-Net Models for Monitoring Elect... 2025 2026 2025 5 10 15

Peers

Fabio La Foresta
Comparison fields: 5 of 117
  • Cognitive Neuroscience 743
  • Signal Processing 423
  • Cardiology and Cardiovascular Medicine 306
  • Biomedical Engineering 203
  • Control and Systems Engineering 115
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Citations per field, relative to Fabio La Foresta
Fabio La Foresta · 1×
Citations per year, relative to Fabio La Foresta
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Countries citing papers authored by Fabio La Foresta

Since Specialization
Citations

This map shows the geographic impact of Fabio La Foresta'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 Fabio La Foresta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabio La Foresta more than expected).

Fields of papers citing papers by Fabio La Foresta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fabio La Foresta. 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 Fabio La Foresta. The network helps show where Fabio La Foresta may publish in the future.

Co-authorship network of co-authors of Fabio La Foresta

This figure shows the co-authorship network connecting the top 25 collaborators of Fabio La Foresta. A scholar is included among the top collaborators of Fabio La Foresta 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 Fabio La Foresta. Fabio La Foresta 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
# Work Indexed citations
1
Integration of LSTM and U-Net Models for Monitoring Electrical Absorption With a System of Sensors and Electronic Circuits breakdown →
17
2 0
3 0
4 9
5 16
6 14
7 0
8 4
9 31
10 6
11 3
12 154
13 195
14 19
15 1
16 0
17 1
18
A New Approach Based On Wavelet-ICA Algorithms For Fetal Electrocardiogram Extraction
45
19 17
20 0

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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