ArticleBrain sciences2022
Optimizing Motor Imagery Parameters for Robotic Arm Control by Brain-Computer Interface.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Motor Imagery Acquisition Paradigms: In the Search to Improve Classification Accuracy.Sensors (Basel, Switzerland) · 2025Article
- Levels of shared autonomy in brain-robot interfaces: enabling multi-robot multi-human collaboration for activities of daily living.Frontiers in human neuroscience · 2025Article
- FB-CCNN: A Filter Bank Complex Spectrum Convolutional Neural Network with Artificial Gradient Descent Optimization.Brain sciences · 2023Article
- Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition.Brain sciences · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Brain-Computer Interface (BCI) technology has been shown to provide new communication possibilities, conveying brain information externally. BCI-based robot control has started to play an important role, especially in medically assistive robots but not only there. For example, a BCI-controlled robotic arm can provide patients diagnosed with neurodegenerative diseases such as Locked-in syndrome (LIS), Amyotrophic lateral sclerosis (ALS), and others with the ability to manipulate different objects. This study presents the optimization of the configuration parameters of a three-class Motor Imagery (MI) -based BCI for controlling a six Degrees of Freedom (DOF) robotic arm in a plane. Electroencephalography (EEG) signals are recorded from 64 positions on the scalp according to the International 10-10 System. In terms of the resulting classification of error rates, we investigated twelve time windows for the spatial filter and classifier calculation and three time windows for the variance smoothing time. The lowest error rates were achieved when using a 3 s time window for creating the spatial filters and classifier, for a variance time window of 1.5 s.
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Registered trials
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