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Treffer: Smart Ward Control Based on a Wearable Multimodal Brain-Computer Interface Mouse.

Title:
Smart Ward Control Based on a Wearable Multimodal Brain-Computer Interface Mouse.
Source:
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society [IEEE Trans Neural Syst Rehabil Eng] 2026; Vol. 34, pp. 638-649.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: IEEE Country of Publication: United States NLM ID: 101097023 Publication Model: Print Cited Medium: Internet ISSN: 1558-0210 (Electronic) Linking ISSN: 15344320 NLM ISO Abbreviation: IEEE Trans Neural Syst Rehabil Eng Subsets: MEDLINE
Imprint Name(s):
Original Publication: Piscataway, NJ : IEEE, c2001-
Entry Date(s):
Date Created: 20260112 Date Completed: 20260122 Latest Revision: 20260123
Update Code:
20260124
DOI:
10.1109/TNSRE.2026.3653138
PMID:
41525552
Database:
MEDLINE

Weitere Informationen

For patients with severe extremity motor function impairment, traditional smart ward control methods, such as those using joysticks and touchscreens, are frequently unsuitable due to their limited physical abilities. Consequently, developing an effective brain-computer interface (BCI) suitable for their operation has become an immediate concern. This paper presents a wearable multimodal BCI system for smart ward control, which employs a self-designed wearable headband to capture head rotation and blinking movement. By wearing the headband, users can control a computer cursor on the screen only with head rotation and blinking, and further control devices in a smart ward with self-designed graphical user interfaces (GUIs). The system decodes signals from an inertial measurement unit (IMU) to map the head posture to the position of the cursor on the screen and decodes electrooculography (EOG) and electroencephalography (EEG) signals to detect valid blinks for selecting and activating function buttons. Ten participants were recruited to perform two experimental tasks that simulate the daily needs of patients with extremity motor function issues. To our satisfaction, all the participants fully accomplished the simulated tasks, and an average accuracy of $97.0\pm 3.9$ % and an average response time of $2.39\pm 0.53$ s were achieved. Different from traditional step-controlled BCI nursing beds, we designed a continuous-controlled nursing bed and achieved satisfactory results. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate the effectiveness of our proposed system, indicating significant potential for practical applications.