ArticleComputational intelligence and neuroscience2018
Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space.
Article in Computational intelligence and neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- A Dimensionality Reduction Approach for Motor Imagery Brain-Computer Interface Using Functional Clustering and Graph Signal Processing.Journal of medical signals and sensors · 2026Article
- Motor imagery-based neural networks for assisting tetraplegic patients.Medical & biological engineering & computing · 2025Article
- Exploring Imagined Movement for Brain-Computer Interface Control: An fNIRS and EEG Review.Brain sciences · 2025Review
- Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery.Brain sciences · 2025Article
- Significance of EEG-electrode combinations while calculating filters with common spatial patterns.German medical science : GMS e-journal · 2024Article
- Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism.Medical & biological engineering & computing · 2023Article
- Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface.BioData mining · 2023Article
- Transfer Learning in Trajectory Decoding: Sensor or Source Space?Sensors (Basel, Switzerland) · 2023Article
- Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity.Frontiers in neuroscience · 2023Article
- Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces.Frontiers in neuroscience · 2023Article
- Neurorehabilitation Through Synergistic Man-Machine Interfaces Promoting Dormant Neuroplasticity in Spinal Cord Injury: Protocol for a Nonrandomized Controlled Trial.JMIR research protocols · 2022Article
- Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills.Sensors (Basel, Switzerland) · 2022Article
- Applying Dimensionality Reduction Techniques in Source-Space ElectroencephalographyFrontiers in human neuroscience · 2022Article
- Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces.Frontiers in neuroscience · 2021Article
- Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns.Entropy (Basel, Switzerland) · 2020Article
- A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery.Brain sciences · 2019Article
- Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework.Sensors (Basel, Switzerland) · 2019Article
- EEG Classification of Motor Imagery Using a Novel Deep Learning Framework.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges include multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble model, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiological knowledge play an important role in developing source space-based BCI algorithms. Feature selection and classifier characteristics of our implementation will be explored to raise performance to current state-of-the-art.
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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.