Mathew Salvaris

1.0k total citations
15 papers, 309 citations indexed

About

Mathew Salvaris is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Signal Processing. According to data from OpenAlex, Mathew Salvaris has authored 15 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Cognitive Neuroscience, 4 papers in Cellular and Molecular Neuroscience and 3 papers in Signal Processing. Recurrent topics in Mathew Salvaris's work include EEG and Brain-Computer Interfaces (12 papers), Neural dynamics and brain function (10 papers) and Neuroscience and Neural Engineering (4 papers). Mathew Salvaris is often cited by papers focused on EEG and Brain-Computer Interfaces (12 papers), Neural dynamics and brain function (10 papers) and Neuroscience and Neural Engineering (4 papers). Mathew Salvaris collaborates with scholars based in United Kingdom and United States. Mathew Salvaris's co-authors include Francisco Sepulveda, Patrick Haggard, Riccardo Poli, Caterina Cinel, Luca Citi, Wee Hyong Tok, Danielle Dean, Archna Bhatia and Sharat Chikkerur and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Neural Systems and Rehabilitation Engineering and Journal of Neural Engineering.

In The Last Decade

Mathew Salvaris

14 papers receiving 300 citations

Peers

Mathew Salvaris
Jeroen Geuze Netherlands
Aravind Ravi Canada
Sarah N. Abdulkader Egypt
Rajesh Singla India
Nupur Chugh India
David Achanccaray Japan
Xiangmin Lun China
Xuelin Ma China
Jeroen Geuze Netherlands
Mathew Salvaris
Citations per year, relative to Mathew Salvaris Mathew Salvaris (= 1×) peers Jeroen Geuze

Countries citing papers authored by Mathew Salvaris

Since Specialization
Citations

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

Fields of papers citing papers by Mathew Salvaris

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mathew Salvaris

This figure shows the co-authorship network connecting the top 25 collaborators of Mathew Salvaris. A scholar is included among the top collaborators of Mathew Salvaris 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 Mathew Salvaris. Mathew Salvaris is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Salvaris, Mathew, et al.. (2020). DeepSeismic: a Deep Learning Library for Seismic Interpretation. 1–5. 4 indexed citations
2.
Salvaris, Mathew, Danielle Dean, & Wee Hyong Tok. (2018). Deep Learning with Azure. Apress eBooks. 17 indexed citations
3.
Salvaris, Mathew, Danielle Dean, & Wee Hyong Tok. (2018). Deep Learning with Azure: Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform. CERN Document Server (European Organization for Nuclear Research). 7 indexed citations
4.
Salvaris, Mathew & Patrick Haggard. (2014). Decoding Intention at Sensorimotor Timescales. PLoS ONE. 9(2). e85100–e85100. 20 indexed citations
5.
Poli, Riccardo, Mathew Salvaris, & Caterina Cinel. (2012). A genetic programming approach to the evolution of brain–computer interfaces for 2-D mouse–pointer control. Genetic Programming and Evolvable Machines. 13(3). 377–405. 2 indexed citations
6.
Salvaris, Mathew, Caterina Cinel, Luca Citi, & Riccardo Poli. (2011). Novel Protocols for P300-Based Brain–Computer Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 20(1). 8–17. 22 indexed citations
7.
Poli, Riccardo, Caterina Cinel, Luca Citi, & Mathew Salvaris. (2011). A genetic programming approach to detecting artifact-generating eye movements from EEG in the absence of electro-oculogram. 416–421. 2 indexed citations
8.
Salvaris, Mathew, Caterina Cinel, & Riccardo Poli. (2011). Novel sequential protocols for a ERP based BCI mouse. 49. 352–355. 2 indexed citations
9.
Salvaris, Mathew & Francisco Sepulveda. (2010). Classification effects of real and imaginary movement selective attention tasks on a P300-based brain–computer interface. Journal of Neural Engineering. 7(5). 56004–56004. 27 indexed citations
10.
Poli, Riccardo, Luca Citi, Mathew Salvaris, Caterina Cinel, & Francisco Sepulveda. (2010). Eigenbrains: The free vibrational modes of the brain as a new representation for EEG. PubMed. 19. 6011–6014. 6 indexed citations
11.
Salvaris, Mathew, Caterina Cinel, Riccardo Poli, Luca Citi, & Francisco Sepulveda. (2010). Exploring multiple protocols for a brain-computer interface mouse. PubMed. 49. 4189–4192. 7 indexed citations
12.
Salvaris, Mathew & Francisco Sepulveda. (2009). Visual modifications on the P300 speller BCI paradigm. Journal of Neural Engineering. 6(4). 46011–46011. 144 indexed citations
13.
Salvaris, Mathew & Francisco Sepulveda. (2009). Wavelets and ensemble of FLDs for P300 classification. 339–342. 36 indexed citations
14.
Salvaris, Mathew & Francisco Sepulveda. (2009). Perceptual errors in the Farwell and Donchin matrix speller. 275–278. 13 indexed citations
15.
Salvaris, Mathew & Francisco Sepulveda. (2007). Robustness of the Farwell & Donchin BCI protocol to visual stimulus parameter changes. Conference proceedings. 113. 2528–2531.

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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