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Published on: September 30, 2020
Portable Brain-Computer Interface for the Intensive Care Unit Patient Communication Using Subject-Dependent SSVEP
Omid Dehzangi1, Muhamed Farooq1
1Computer and Information Science Department, University of Michigan-Dearborn, 4901 Evergreen Rd., CIS 112, Dearborn, MI, USA.
This study introduces a Brain-Computer Interface for Intensive Care Unit (ICU) communications (BCI4ICU), improving patient-device interaction. The novel system achieved 98.7% accuracy, enhancing communication for critical care patients.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Critical Care Medicine
Background:
- Intensive Care Unit (ICU) patients often face communication barriers, leading to distress and unrecognized needs.
- Existing communication methods in ICUs can be inconsistent and ineffective.
- Brain-Computer Interface (BCI) systems offer a potential solution but face challenges in real-world applications.
Purpose of the Study:
- To develop and evaluate a portable Brain-Computer Interface system for Intensive Care Unit (ICU) communications (BCI4ICU).
- To enhance communication effectiveness and consistency for ICU patients.
- To address limitations of current BCI systems in clinical environments.
Main Methods:
- Designed a portable BCI system (BCI4ICU) using a wearable EEG cap and an Android app for visual stimuli and data processing.
- Proposed a novel subject-specific Gaussian Mixture Model- (GMM-) based training and adaptation algorithm.
- Incorporated subject-specific information into the SSVEP identification model training and adaptation using GMMs.
Main Results:
- The BCI4ICU system achieved an average identification accuracy of 98.7%.
- Subject-specific GMMs demonstrated effectiveness in improving model performance.
- The system successfully generated high-dimensional supervectors for prediction.
Conclusions:
- The developed BCI4ICU system shows significant promise for improving communication in intensive care settings.
- The novel GMM-based training and adaptation algorithm enhances BCI performance in real-world scenarios.
- This technology can lead to more consistent and effective communication for critically ill patients.

