ArticleJournal of medical systems2019
Improvement Motor Imagery EEG Classification Based on Regularized Linear Discriminant Analysis.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 72 citations in OpenAlex.
- PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Lightweight deep learning model for nonconvulsive status epilepticus diagnosis using EEG time-frequency analysis.Scientific reports · 2026Article
- DO-PI-EATCNet: Efficient-Attention- and Dream-Optimization-Based Channel Selection for EEG Motor Imagery Classification.Sensors (Basel, Switzerland) · 2026Article
- Hierarchical attention enhanced deep learning achieves high precision motor imagery classification in brain computer interfaces.Scientific reports · 2025Article
- GAH-TNet: A Graph Attention-Based Hierarchical Temporal Network for EEG Motor Imagery Decoding.Brain sciences · 2025Article
- CLTNet: A Hybrid Deep Learning Model for Motor Imagery Classification.Brain sciences · 2025Article
- Time-frequency-space transformer EEG decoding for spinal cord injury.Cognitive neurodynamics · 2024Article
- Tensor decomposition-based channel selection for motor imagery-based brain-computer interfaces.Cognitive neurodynamics · 2024Article
- EMPT: a sparsity Transformer for EEG-based motor imagery recognition.Frontiers in neuroscience · 2024Article
- Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface.Frontiers in neuroinformatics · 2022Article
- A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations.Computational intelligence and neuroscience · 2021Article
- Intentions Recognition of EEG Signals with High Arousal Degree for Complex Task.Journal of medical systems · 2020Article
- A performance based feature selection technique for subject independent MI based BCI.Health information science and systems · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Mental tasks classification such as motor imagery, based on EEG signals is an important problem in brain computer interface systems (BCI). One of the major concerns in BCI is to have a high classification accuracy. The other concerning one is with the favorable result is guaranteed how to improve the computational efficiency. In this paper, Mu/Beta rhythm was obtained by bandpass filter from EEG signal. And the classical linear discriminant analysis (LDA) was used for deciding which rhythm can give the better classification performance. During this, the common spatial pattern (CSP) was used to project data subject to the ratio of projected energy of one class to that of the other class was maximized. The optimal projection dimension was determined corresponding to the maximum of area under the curve (AUC) for each participant. Eventually, regularized linear discriminant analysis (RLDA) is possible to decode the imagined motor sensed using electroencephalogram (EEG). Results show that higher classification accuracy can be provided by RLDA. And optimal projection dimensions determined by LDA and RLDA are of consistent solution, this improves computational efficiency of CSP-RLDA method without computation of projection dimension.
Indexed as
Identifiers
What OpenQuestion holds
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.