Trial reportJournal of neuroengineering and rehabilitation2017
Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair.
Trial report in Journal of neuroengineering and rehabilitation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model.Sensors (Basel, Switzerland) · 2024Article
- Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities.Heliyon · 2024Article
- Effect of 3D paradigm synchronous motion for SSVEP-based hybrid BCI-VR system.Medical & biological engineering & computing · 2023Article
- An improved model using convolutional sliding window-attention network for motor imagery EEG classification.Frontiers in neuroscience · 2023Article
- Mini-review: Robotic wheelchair taxonomy and readiness.Neuroscience letters · 2022Review
- Evaluation of Switch and Continuous Navigation Paradigms to Command a Brain-Controlled Wheelchair.Frontiers in neuroscience · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCertain diseases affect brain areas that control the movements of the patients' body, thereby limiting their autonomy and communication capacity. Research in the field of Brain-Computer Interfaces aims to provide patients with an alternative communication channel not based on muscular activity, but on the processing of brain signals. Through these systems, subjects can control external devices such as spellers to communicate, robotic prostheses to restore limb movements, or domotic systems. The present work focus on the non-muscular control of a robotic wheelchair.
methodA proposal to control a wheelchair through a Brain-Computer Interface based on the discrimination of only two mental tasks is presented in this study. The wheelchair displacement is performed with discrete movements. The control signals used are sensorimotor rhythms modulated through a right-hand motor imagery task or mental idle state. The peculiarity of the control system is that it is based on a serial auditory interface that provides the user with four navigation commands. The use of two mental tasks to select commands may facilitate control and reduce error rates compared to other endogenous control systems for wheelchairs.
resultsSeventeen subjects initially participated in the study; nine of them completed the three sessions of the proposed protocol. After the first calibration session, seven subjects were discarded due to a low control of their electroencephalographic signals; nine out of ten subjects controlled a virtual wheelchair during the second session; these same nine subjects achieved a medium accuracy level above 0.83 on the real wheelchair control session.
conclusionThe results suggest that more extensive training with the proposed control system can be an effective and safe option that will allow the displacement of a wheelchair in a controlled environment for potential users suffering from some types of motor neuron diseases.
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.