Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 643

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

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

Lemm, S., Blankertz, B., Curio, G., Muller, K.-R.·2005·IEEE Transactions on Biomedical Engineering

Abstract

Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, non-stationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tbme.2005.851521

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Cited by

See all 643

Metadata

Authors
Lemm, S., Blankertz, B., Curio, G., & Muller, K.-R.
Venue
IEEE Transactions on Biomedical Engineering
Published
2005-09
Volume
52
Issue
9
Pages
1541-1548
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
OpenAlex
W2131321253
Cited by
643
References
4
FWCI
10.27
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Spatio-spectral filters for improving the classification of single trial EEG. https://app.cochat.ai/papers/10.1109/tbme.2005.851521
The paper
Lemm, S., Blankertz, B., Curio, G., & Muller, K.-R. (2005). Spatio-spectral filters for improving the classification of single trial EEG. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2005.851521

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.