ArticlePloS one2019
A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- Subject-Specific Deep Learning Model for Motor Imagery Direction Decoding.Sensors (Basel, Switzerland) · 2026Article
- Exploration-based feedback for BCI training: a case study with an adolescent with paraplegia.Journal of neuroengineering and rehabilitation · 2026Article
- Canonical coherence for the estimation of within- and cross-frequency cortico-kinematic interactions.Scientific reports · 2026Article
- Cognitive load gating system in motor imagery BCIs: a dual-task EEG study with differential entropy-based reliability estimation.Frontiers in artificial intelligence · 2026Article
- Proficiency in motor imagery is linked to the lateralization of focused ERD patterns and beta PDC.Journal of neuroengineering and rehabilitation · 2025Article
- CLTNet: A Hybrid Deep Learning Model for Motor Imagery Classification.Brain sciences · 2025Article
- Cardiac cycle modulates alpha and beta suppression during motor imagery.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Identifying good practices for detecting inter-regional linear functional connectivity from EEG.NeuroImage · 2023Article
- Subject Separation Network for Reducing Calibration Time of MI-Based BCI.Brain sciences · 2023Article
- Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety.Neural computing & applications · 2023Article
- Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.Frontiers in computational neuroscience · 2023Article
- Review of public motor imagery and execution datasets in brain-computer interfaces.Frontiers in human neuroscience · 2023Review
- Challenges of neural interfaces for stroke motor rehabilitation.Frontiers in human neuroscience · 2023Article
- Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain-Computer Interface.Entropy (Basel, Switzerland) · 2022Article
- Basal ganglia-cortical connectivity underlies self-regulation of brain oscillations in humans.Communications biology · 2022Article
- Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?Frontiers in aging neuroscience · 2022Review
- Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- Deep and Wide Transfer Learning with Kernel Matching for Pooling Data from Electroencephalography and Psychological Questionnaires.Sensors (Basel, Switzerland) · 2021Article
- Success of Hand Movement Imagination Depends on Personality Traits, Brain Asymmetry, and Degree of Handedness.Brain sciences · 2021Article
- Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.
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.