Hubert Cecotti

98 papers receiving 2.6k citations

Hit Papers

Convolutional Neural Networks for P300 Detection with App...20102026201520202010100200300400500

Peers

Hubert Cecotti
Comparison fields: 5 of 115
  • Cognitive Neuroscience 2.1k
  • Cellular and Molecular Neuroscience 878
  • Human-Computer Interaction 670
  • Electrical and Electronic Engineering 529
  • Signal Processing 429
Replace Bruno Arnaldi with:
Bruno Arnaldi France
Steven Lemm Germany
Herbert Ramoser Austria
Mehrdad Fatourechi Canada
Matthias Krauledat Germany
Erwei Yin China
Martin Bogdan Germany
Tianyou Yu China
Gary E. Birch Canada
Katharina Eggensperger Germany
Hubert Cecotti relative to Bruno Arnaldi France Bruno Arnaldi's profile →
Citations per field
00.5×1.5×2.1×
Bruno Arnaldi · 1×
Citations per year

Countries citing papers authored by Hubert Cecotti

Since Specialization
Citations

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

Fields of papers citing papers by Hubert Cecotti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hubert Cecotti. 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 Hubert Cecotti. The network helps show where Hubert Cecotti may publish in the future.

Co-authorship network of co-authors of Hubert Cecotti

This figure shows the co-authorship network connecting the top 25 collaborators of Hubert Cecotti. A scholar is included among the top collaborators of Hubert Cecotti 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 Hubert Cecotti. Hubert Cecotti 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
#WorkIndexed citations
1 0
2 13
3 1
4 0
5 79
6 4
7 32
8 2
9 33
10 21
11 37
12 6
13 125
14 74
15 21
16 17
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Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfacesbreakdown →
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18 16
19
Neural network pruning for feature selection Application to a P300 Brain-Computer Interface
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20
Time Delay Neural Network with Fourier transform for multiple channel detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces
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About Hubert Cecotti

Hubert Cecotti is a scholar working on Human-Computer Interaction, Cognitive Neuroscience and Cellular and Molecular Neuroscience, having authored 103 papers that have together received 2.7k indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (72 papers), Neural dynamics and brain function (32 papers) and Neuroscience and Neural Engineering (28 papers). The work is most often cited by research in Human-Computer Interaction (670 citations), Cognitive Neuroscience (2.1k citations) and Cellular and Molecular Neuroscience (878 citations). Hubert Cecotti has collaborated with scholars based in United States, United Kingdom and France. Frequent co-authors include Anita Graser, Girijesh Prasad, Haider Raza, Barry Giesbrecht, Yogesh Kumar Meena, Dheeraj Rathee, Axel Gräser, Miguel P. Eckstein, Ivan Volosyak and Anthony J. Ries. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neuroscience and Expert Systems with Applications.

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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