The primary means of communication for these patients is the use of nonvocal techniques, such as lip reading and gestures, which are often inadequate for effective communication with family and ICU staff. Mechanically ventilated patients in the intensive care are voiceless and unable to communicate their needs verbally and their inability to communicate adequately can lead to fear, panic, and insecurity. Furthermore, the inability to communicate with caregivers hampers the ability of critically ill patients to be active participants in their treatment and in decision-making, including decisions to withdraw or withhold life-sustaining treatment. This inability to communicate effectively can lead to the inappropriate use of sedatives and prolongation of time spent on the ventilator, which may then lead to increased ICU length of stay and costs. Caregivers also frequently report feeling anxious and frustrated in not being able to adequately assess the needs of their patients. Patients in the ICU therefore commonly suffer unrecognized pain and discomfort and feelings of loss of control and insecurity, depersonalization, anxiety, sleep disturbances, fear, and frustration. Nurses initiate about 86% of all communication exchanges as it is typically very difficult for a voiceless patient in the intensive care to initiate communication.
Patients rate about 40% of communication sessions as difficult and more than a third of communications about pain as unsuccessful. IntroductionĪ major problem for mechanically ventilated patients in the Intensive Care Unit (ICU) is their inability to consistently and effectively communicate their most fundamental physical needs. Our experimental results demonstrate that the proposed system achieved 98.7% average identification accuracy, which is promising in order to provide effective and consistent communication for patients in the intensive care. Finally, the adapted mixture mean scores of the subject-specific GMMs are utilized to generate the high-dimensional supervectors. Subsequently, from the GMM discriminative scores, we generate the transformed vectors, which are passed to our predictive model. We evaluate subject-specific models against other subjects. First, we incorporate subject-specific information in the training phase of the SSVEP identification model using GMM-based training and adaptation. Furthermore, to overcome the challenges that BCI systems face today in real-world scenarios, we propose a novel subject-specific Gaussian Mixture Model- (GMM-) based training and adaptation algorithm.
#Thebrain 9 portable android#
The system utilizes a wearable EEG cap coupled with an Android app designed on a mobile device that serves as visual stimuli and data processing module.
#Thebrain 9 portable portable#
As such, we designed a portable BCI system for ICU communications (BCI4ICU) optimized to operate effectively in an ICU environment. This, in turn, can cause distress, unrecognized pain, anxiety, and fear. Patients rated most communication sessions as difficult and unsuccessful. A major predicament for Intensive Care Unit (ICU) patients is inconsistent and ineffective communication means.