Spatio-spectral filters for improving the classification of single trial EEG

503 indexed citations

Abstract

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This paper, published in 2005, received 503 indexed citations. Written by S. Lemm, Benjamin Blankertz, Gabriel Curio and K. Müller covering the research area of Cognitive Neuroscience and Signal Processing. It is primarily cited by scholars working on Cognitive Neuroscience (487 citations), Signal Processing (245 citations) and Cellular and Molecular Neuroscience (191 citations). Published in IEEE Transactions on Biomedical Engineering.

Countries where authors are citing Spatio-spectral filters for improving the classification of single trial EEG

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Citations

This map shows the geographic impact of Spatio-spectral filters for improving the classification of single trial EEG. 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 Spatio-spectral filters for improving the classification of single trial EEG with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Spatio-spectral filters for improving the classification of single trial EEG more than expected).

Fields of papers citing Spatio-spectral filters for improving the classification of single trial EEG

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Spatio-spectral filters for improving the classification of single trial EEG. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Spatio-spectral filters for improving the classification of single trial EEG.

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.

This paper is also available at doi.org/10.1109/tbme.2005.851521.

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