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Aims: Inattention in young people is one of the main reasons for their declining learning ability. Frontoparietal networks (FPNs) are associated with attention and executive function. Brain computer interface (BCI) training has been applied in neurorehabilitation, but there is a lack of research on its application to cognition. This study aimed to investigate the effect of BCI on the attention network in healthy young adults.
Methods: Twenty-seven healthy people performed BCI training for 5 consecutive days. An attention network test (ANT) was performed at baseline and immediately after the fifth day of training and included simultaneous functional near-infrared spectroscopy recording.
Results: BCI performance improved significantly after BCI training (p = 0.005). The efficiencies of the alerting and executive control networks were enhanced after BCI training (p = 0.032 and 0.003, respectively). The functional connectivity in the bilateral prefrontal cortices and the right posterior parietal cortex increased significantly after BCI training (p < 0.05).
Conclusion: Our findings suggested that repetitive BCI training could improve attention and induce lasting neuroplastic changes in FPNs. It might be a promising rehabilitative strategy for clinical populations with attention deficits. The right PPC may also be an effective target for neuromodulation in diseases with attention deficits.
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http://dx.doi.org/10.1111/cns.70400 | DOI Listing |
Adv Sci (Weinh)
September 2025
School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China.
Brain-computer interfaces (BCIs) enable communication between individuals and computers or other assistive devices by decoding brain activity, thereby reconstructing speech and motor functions for patients with neurological disorders. This study presents a high-resolution micro-electrocorticography (µECoG) BCI based on a flexible, high-density µECoG electrode array, capable of chronically stable and real-time motor decoding. Leveraging micro-nano manufacturing technology, the µECoG BCI achieves a 64-fold increase in electrode density compared to conventional clinical electrode arrays, enhancing spatial resolution while featuring scalability.
View Article and Find Full Text PDFNeuroimage
September 2025
Department of Human and Animal Physiology, Faculty of Biology, Lomonosov Moscow State University, 119234, Moscow, Russia.
Tactile imagery (TI) engages somatosensory cortices in both hemispheres, along with widespread brain regions associated with the imagery process itself. Actively simulating touch can influence the processing of actual tactile stimuli, as reflected by modulations in somatosensory evoked potentials (SEPs) components. This study uses high-density electroencephalography (EEG) and sLORETA-based source localization to analyse cortical sources of SEPs components susceptible to active skin sensations imagery.
View Article and Find Full Text PDFFront Neurorobot
August 2025
Technology Research Institute, Arrow Technology Company, ZhuHai, China.
Brain-computer interface (BCI) integration with virtual reality (VR) has progressed from single-limb control to multi-limb coordination, yet achieving intuitive tri-manual operation remains challenging. This study presents a consumer-grade hybrid BCI-VR framework enabling simultaneous control of two biological hands and a virtual third limb through integration of Tobii eye-tracking, NeuroSky single-channel EEG, and non-haptic controllers. The system employs e-Sense attention thresholds (>80% for 300 ms) to trigger virtual hand activation combined with gaze-driven targeting within 45° visual cones.
View Article and Find Full Text PDFJ Neuroeng Rehabil
August 2025
The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Gulou District, Nanjing City, Jiangsu Province, China.
Background: Brain-computer interface (BCI) has been shown to be beneficial in improving lower limb motility in stroke, but their effectiveness on balance and attention is unclear. In addition, current BCIs are mostly in single-task mode. The BCI system used in this study was based on a dual-task model of motor imagery (MI) and virtual reality (VR).
View Article and Find Full Text PDFCureus
July 2025
Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.
Brain-computer interfaces (BCIs) represent an emerging advancement in rehabilitation, enabling direct communication between the brain and external devices to aid recovery in individuals with neurological impairments. BCIs can be classified into invasive, semi-invasive, non-invasive, or hybrid types. By interpreting neural signals and converting them into control commands, BCIs can bypass damaged pathways, offering therapeutic potential for conditions such as stroke, spinal cord injury, traumatic brain injury, and neurodegenerative diseases such as amyotrophic lateral sclerosis.
View Article and Find Full Text PDF