Although EEG experiments over the past decades have shown numerous applications for brain-computer interfacing (BCI), there is a need for user-friendly BCI devices that can be used in real-world situations. 3D anthropometry and statistical shape modeling have been shown to improve the fit of devices such as helmets and respirators, and thus they might also be suitable to design BCI headgear that better fits the size and shape variation of the human head. In this paper, a new design method for BCI devices is proposed and evaluated. A one-size-fits-all BCI headset frame is designed on the basis of three digital mannequins derived from a shape model of the human head. To verify the design, the geometric fit, stability and repeatability of the prototype were compared to an EEG cap and a commercial BCI headset in a preliminary experiment. Most design specifications were met, and all the results were found to be similar to those of the commercial headset. Therefore, the suggested design method is a feasible alternative to traditional anthropometric design for BCI headsets and similar headgear.
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http://dx.doi.org/10.1016/j.apergo.2016.06.002 | DOI Listing |
J Neural Eng
January 2025
Department of Biomedical Engineering, The University of Melbourne, Parkville, Melbourne, Victoria, 3010, AUSTRALIA.
Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A Brain-Computer Interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair.
View Article and Find Full Text PDFJ Neural Eng
January 2025
University of Pittsburgh, 1622 Locust St, Pittsburgh, Pennsylvania, 15219, UNITED STATES.
Real-world implementation of brain-computer interfaces (BCI) for continuous control of devices should ideally rely on fully asynchronous decoding approaches. That is, the decoding algorithm should continuously update its output by estimating the user's intended actions from real-time neural activity, without the need for any temporal alignment to an external cue. This kind of open-ended temporal flexibility is necessary to achieve naturalistic and intuitive control, but presents a challenge: how do we know when it is appropriate to decode anything at all? Activity in motor cortex is dynamic and modulates with many different types of actions (proximal arm control, hand control, speech, etc.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 Valencia, Spain.
In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Electronics and Communication Engineering, Istanbul Technical University, 34467 Istanbul, Istanbul, Turkey.
Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain-Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone.
View Article and Find Full Text PDFBiomed Eng Lett
January 2025
School of Chemistry and Chemical Engineering, Tianjin University of Technology, Tianjin, 300384 People's Republic of China.
Brain-computer interface (BCI) has been widely used in human-computer interaction. The introduction of artificial intelligence has further improved the performance of BCI system. In recent years, the development of BCI has gradually shifted from personal computers to embedded devices, which boasts lower power consumption and smaller size, but at the cost of limited device resources and computing speed, thus can hardly improve the support of complex algorithms.
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