On the public citation graph

EEG feature extraction using common spatial pattern with spectral graph decomposition

https://doi.org/10.1109/iccni.2017.8123799
0 signed · 39 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/wics.101openalex-snapshot
Principal component analysis
inferredcites →https://doi.org/10.1007/978-1-4020-5614-7_2569openalex-snapshot
Pearson’s Correlation Coefficient
inferredcites →https://doi.org/10.1007/978-3-662-12494-9openalex-snapshot
Metric Spaces of Non-Positive Curvature
inferredcites →https://doi.org/10.1007/bf01129656openalex-snapshot
Spatial patterns underlying population differences in the background EEG
inferredcites →https://doi.org/10.1016/0013-4694(91)90163-xopenalex-snapshot
The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG
inferredcites →https://doi.org/10.1016/0024-3795(94)90486-3openalex-snapshot
Laplacian matrices of graphs: a survey
inferredcites →https://doi.org/10.1016/s1388-2457(98)00038-8openalex-snapshot
Designing optimal spatial filters for single-trial EEG classification in a movement task
inferredcites →https://doi.org/10.1017/cbo9780511615337.008openalex-snapshot
Correlation and Dependence in Risk Management: Properties and Pitfalls
inferredcites →https://doi.org/10.1049/htl.2013.0010openalex-snapshot
Temporal epilepsy seizures monitoring and prediction using cross‐correlation and chaos theory
inferredcites →https://doi.org/10.1088/1741-2560/9/4/045009openalex-snapshot
The analytic common spatial patterns method for EEG-based BCI data
inferredcites →https://doi.org/10.1090/cbms/092openalex-snapshot
Spectral Graph Theory
inferredcites →https://doi.org/10.1109/86.895946openalex-snapshot
Optimal spatial filtering of single trial EEG during imagined hand movement
inferredcites →https://doi.org/10.1109/cne.2007.369647openalex-snapshot
Sub-band Common Spatial Pattern (SBCSP) for Brain-Computer Interface
inferredcites →https://doi.org/10.1109/iembs.2005.1615701openalex-snapshot
Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface
inferredcites →https://doi.org/10.1109/tac.2004.834433openalex-snapshot
Information Flow and Cooperative Control of Vehicle Formations
inferredcites →https://doi.org/10.1109/tbme.2010.2082539openalex-snapshot
Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms
inferredcites →https://doi.org/10.1109/tbme.2012.2205246openalex-snapshot
Complex-Valued Spatial Filters for SSVEP-Based BCIs With Phase Coding
inferredcites →https://doi.org/10.1142/s0129065713500147openalex-snapshot
APPLICATION OF HIGHER ORDER CUMULANT FEATURES FOR CARDIAC HEALTH DIAGNOSIS USING ECG SIGNALS
inferredcites →https://doi.org/10.1145/1014052.1014118openalex-snapshot
Kernel k-means
inferredcites →https://doi.org/10.1145/1655925.1656052openalex-snapshot
A wavelet based method for detecting and localizing epileptic neural spikes in EEG
inferredcites →https://doi.org/10.1145/2485984.2485990openalex-snapshot
Miniaturized wireless ECG monitor for real-time detection of epileptic seizures
inferredcites →https://doi.org/10.1161/01.cir.101.23.e215openalex-snapshot
PhysioBank, PhysioToolkit, and PhysioNet
inferredcites →https://doi.org/10.1175/1520-0450(1984)023<1660:cpcata>2.0.co;2openalex-snapshot
Complex Principal Component Analysis: Theory and Examples
inferredcites →https://doi.org/10.2307/2531822openalex-snapshot
Kendall's and Spearman's Correlation Coefficients in the Presence of a Blocking Variable
inferredcites →https://doi.org/10.3109/03091902.2011.634944openalex-snapshot
Development of a secure body area network for a wearable physiological monitoring system using a PSoC processor
inferredcites →https://doi.org/10.4218/etrij.12.0112.0520openalex-snapshot
A Max-Flow-Based Similarity Measure for Spectral Clustering

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4170113openalex-extracted
inferred← citeshttps://doi.org/10.1007/978-3-031-31153-6_49openalex-snapshot
The Numerical Estimation of Spectral Energy Density in Electroencephalogram (EEG)
inferred← citeshttps://doi.org/10.1016/j.bspc.2019.101748openalex-snapshot
A wrapped time-frequency combined selection in the source domain
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106498openalex-snapshot
A Dynamical Graph-Based Feature Extraction Approach to Enhance Mental Task Classification in Brain-Computer Interfaces
inferred← citeshttps://doi.org/10.1109/ecace.2019.8679463openalex-snapshot
Frequency Domain Approach in CSP based Feature Extraction for EEG Signal Classification
inferred← citeshttps://doi.org/10.48550/arxiv.1810.02223openalex-snapshot
Brain2Object: Printing Your Mind from Brain Signals with Spatial Correlation Embedding
inferred← citeshttps://doi.org/10.7287/peerj-cs.1029v0.1/reviews/1openalex-snapshot
Peer Review #1 of "A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1029v0.1/reviews/2openalex-snapshot
Peer Review #2 of "A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1029v0.1/reviews/3openalex-snapshot
Peer Review #3 of "A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1029v0.2/reviews/1openalex-snapshot
Peer Review #1 of "A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1029v0.2/reviews/2openalex-snapshot
Peer Review #2 of "A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1029v0.2/reviews/4openalex-snapshot
Peer Review #4 of "A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj-cs.1029openalex-snapshot
A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.