ReviewBehavior research methods2023
Channel selection from source localization: A review of four EEG-based brain-computer interfaces paradigms.
Review in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A patient-specific, 3D printed neurostimulator enabling focal transcranial electrical stimulation and EEG at home.Journal of neuroengineering and rehabilitation · 2025Trial
- PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction Accuracy.Bioengineering (Basel, Switzerland) · 2024Article
- Design Decisions for Wearable EEG to Detect Motor Imagery Movements.Sensors (Basel, Switzerland) · 2024Article
- Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.Sensors (Basel, Switzerland) · 2024Article
- Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.IEEE journal of translational engineering in health and medicine · 2024Article
- Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI.Sensors (Basel, Switzerland) · 2023Article
- Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.Physical and engineering sciences in medicine · 2023Article
- Multidomain Convolution Neural Network Models for Improved Event-Related Potential Classification.Sensors (Basel, Switzerland) · 2023Article
- Electroencephalography source localization.Clinical and experimental pediatrics · 2023Article
- Exploiting the Cone of Influence for Improving the Performance of Wavelet Transform-Based Models for ERP/EEG Classification.Brain sciences · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Channel selection is a critical part of the classification procedure for multichannel electroencephalogram (EEG)-based brain-computer interfaces (BCI). An optimized subset of electrodes reduces computational complexity and optimizes accuracy. Different tasks activate different sources in the brain and are characterized by distinctive channels. The goal of the current review is to define a subset of electrodes for each of four popular BCI paradigms: motor imagery, motor execution, steady-state visual evoked potentials and P300. Twenty-one studies have been reviewed to identify the most significant activations of cortical sources. The relevant EEG sensors are determined from the reported 3D Talairach coordinates. They are scored by their weighted mean Cohen's d and its confidence interval, providing the magnitude of the corresponding effect size and its statistical significance. Our goal is to create a knowledge-based channel selection framework with a sufficient statistical power. The core channel selection (CCS) could be used as a reference by EEG researchers and would have the advantages of practicality and rapidity, allowing for an easy implementation of semiparametric algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.