ArticleScientific reports2021
High-wearable EEG-based distraction detection in motor rehabilitation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Wearable technologies for assisted mobility in the real world.Nature communications · 2025Review
- EEG-Based Engagement Monitoring in Cognitive Games.Sensors (Basel, Switzerland) · 2025Article
- Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis.Sensors (Basel, Switzerland) · 2024Article
- Solvent-Free and Cost-Efficient Fabrication of a High-Performance Nanocomposite Sensor for Recording of Electrophysiological Signals.Biosensors · 2024Article
- Executive Functions Assessment Based on Wireless EEG and 3D Gait Analysis During Dual-Task: A Feasibility Study.IEEE journal of translational engineering in health and medicine · 2024Article
- Integrating EEG biomarkers in the neurocognitive screening of executive functions in substance and behavioral addiction.Frontiers in psychiatry · 2024Article
- Uncovering the Correlation between COVID-19 and Neurodegenerative Processes: Toward a New Approach Based on EEG Entropic Analysis.Bioengineering (Basel, Switzerland) · 2023Article
- Assessment and Scientific Progresses in the Analysis of Olfactory Evoked Potentials.Bioengineering (Basel, Switzerland) · 2022Review
- EEG-based detection of emotional valence towards a reproducible measurement of emotions.Scientific reports · 2021Article
- Sensors for Context-Aware Smart Healthcare: A Security Perspective.Sensors (Basel, Switzerland) · 2021Review
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient's attention for enhancing the therapy effectiveness.
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