ArticleSensors (Basel, Switzerland)2023
Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 16 citations in OpenAlex.
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Executive Function Assessment Through Wireless EEG and Gait Monitoring During Cognitive-Motor Dual Tasks.Annals of biomedical engineering · 2026Article
- Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification.Frontiers in neuroscience · 2026Article
- Music-evoked EEG signals for mental health assessment using two- and three-class classification.Frontiers in systems neuroscience · 2026Article
- Review
- Classification of Different Motor Imagery Tasks with the Same Limb Using Electroencephalographic Signals.Sensors (Basel, Switzerland) · 2025Article
- Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.Scientific reports · 2025Article
- Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform.Sensors (Basel, Switzerland) · 2025Article
- A Bibliometric Analysis of the Application of Brain-Computer Interface in Rehabilitation Medicine Over the Past 20 Years.Journal of multidisciplinary healthcare · 2025Article
- Increasing accessibility to a large brain-computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification.Data in brief · 2024Article
- Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.Frontiers in neuroscience · 2024Article
- Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
Abstract
The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain-computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.
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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.