ArticleBrain computer interfaces (Abingdon, England)2024
Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.
Article in Brain computer interfaces (Abingdon, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- EEG coherence signatures of sport-specific motor imagery in elite freestyle skiing aerials athletes.Frontiers in neuroscience · 2026Article
- Individualized brain-computer interface for people with disabilities: a review.Frontiers in human neuroscience · 2026Review
- Comparison of Classifier Calibration Schemes for Movement Intention Detection in Individuals with Cerebral Palsy for Inducing Plasticity with Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Article
- Design and Demonstration of a Hybrid FES-BCI-Based Robotic Neurorehabilitation System for Lower Limbs.Sensors (Basel, Switzerland) · 2025Article
- Field-Programmable Gate Array-Based Ultra-Low Power Discrete Fourier Transforms for Closed-Loop Neural Sensing.bioRxiv : the preprint server for biology · 2025Article
- Neurorehabilitation technologies and functional recovery after brain injury: influence of sex, an integrative review.Frontiers in digital health · 2025Review
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Authors and funding
7 authors.
Funding
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Abstract
The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as 'BCI inefficiency'. Although past research has attempted to find a solution by investigating factors influencing users' MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user's ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance.
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