Bridging the Communication Gap: Cost-Effective Brain and Body Interfaces for Assistive Communication
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| I whakaputaina i: | ProQuest Dissertations and Theses (2025) |
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| I whakaputaina: |
ProQuest Dissertations & Theses
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| Urunga tuihono: | Citation/Abstract Full Text - PDF |
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| Whakarāpopotonga: | Assistive communication technologies have significantly improved accessibility for individuals with motor impairments; however, developing solutions that are accessible, cost-effective, and adaptable remains a challenge. Many existing systems fail to accommodate users' diverse and evolving needs, often lacking affordability, flexibility, and ease of use. This dissertation addresses these challenges by integrating three non-invasive, cost-effective modalities—electromyography (EMG) for gesture-based control, electrooculography (EOG) for ocular-based input, and a non-invasive electroencephalography (EEG)-based brain-computer interface (BCI)— into the Assistive Context-Aware Toolkit (ACAT), an open-source augmentative and alternative communication (AAC) platform developed at Intel Labs. Originally created for Professor Stephen Hawking, ACAT features advanced language models, a user-centered design, multimodal input support, and an open-source framework that fosters continuous innovation and community engagement. By expanding ACAT’s capabilities with these modalities, this research bridges the communication gap for individuals with varying levels of motor impairment. EMG functions as a switch, detecting muscle activations for real-time communication. Sensors can be placed on different muscle groups, allowing for customizable configurations tailored to user comfort and needs. Compared to camera-based and proximity-based switches, EMG demonstrates greater adaptability and reliability, particularly for users with fluctuating motor control. EOG provides a cost-effective, non-invasive solution for ocular-controlled communication. It integrates with a specially designed interface to enhance typing efficiency through optimized layouts and word prediction. When evaluated against traditional eye-tracking systems, EOG demonstrated reduced fatigue and consistent performance, offering a viable alternative with improved usability and comfort. The integration of BCI technology represents a significant advancement in assistive communication, providing a direct, non-muscular control channel for users in locked-in syndrome (LIS) or those with progressive conditions such as amyotrophic lateral sclerosis (ALS). The BCI system is fully integrated into ACAT, leveraging advanced language models and a user-centered design to enhance interaction efficiency. Utilizing cost-effective, commercially available EEG sensors alongside robust signal processing and machine learning techniques, the system facilitates accurate neural signal detection. The BCI-enabled ACAT platform is now open source and available to users. This work advances assistive communication by providing adaptable, cost-effective, and user-centered solutions while fostering global innovation through its open-source framework. These contributions lay the groundwork for more inclusive technologies that adapt to diverse and evolving user needs, ensuring communication remains accessible to all. |
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| ISBN: | 9798314844489 |
| Puna: | ProQuest Dissertations & Theses Global |