A Cross-Platform EEG Monitoring and Processing Application for Mobile Devices
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Abstract
Background: The acquisition of electrophysiological signals in multiple environments is becoming daily practice. In the last decade, hospitals have integrated Information and Communication Technologies in their facilities. In this context, conventional methods do not allow sufficient mobility of the patient or physician.
The deployment of new communication infrastructures and the use of mobile devices to provide medical services have changed the paradigm of e-health. Wireless networks (WLAN) extend the conventional concept of bedside monitoring to ambulatory monitoring, providing a new paradigm of patient care, whether in hospital or home care.
This situation has led to the proliferation of several software tools to acquire and process different signals. Unfortunately, not all software is able to interact with all types of acquisition hardware. Additionally, most software tools are proprietary or are based on proprietary software.
Objective: to develop a cross-platform m-health application to view and analyze EEG data.
Methods: The application is written in the cross-platform Python programming language and uses the free and open source Kivy libraries for developing multi-touch application software with a natural user interface (NUI). Thus, it runs over Android and iOS as well as in desktop operating systems (Windows, Linux, MacOS X).
Our application can communicate wirelessly with proprietary and non-proprietary standards of different EEG systems. Its main functionality includes both client and server functionality over TCP/IP, to receive retransmitted data from acquisition devices and to allow for peer-to-peer connection respectively.
The application also includes several basic signal processing algorithms, including DC removal and frequency filters. Additionally, it allows to read data from stored files and generate synthetic signals (for testing purposes).
The application is modular; the chosen programming language and libraries provide inherent cross-platform capabilities to ensure portability over different devices. This implementation allows mobility for ubiquitous data access and pervasive healthcare.
Results: We are currently testing our application for different EEG devices, such as the EEG Brain Vision system, and on different Android Smartphones and tablets, such as the Samsung Galaxy S III and the Samsung Galaxy Tab 2, and Ubuntu laptops.
We are also validating the viewer in simultaneous EEG-fMRI (Electroencephalography and functional Magnetic Resonance Imaging) acquisition. The EEG signals and fMRI images are acquired with the EEG Brain Vision system and a GE Signa HDxt 3.0T respectively. We use a Field Programmable Gate Array (FPGA) for real-time artifact (imaging, ballistocardiographic and eye movement artifacts) rejection, based on acquisition information received from the MRI scanner.
These results are being validated at the Research Center for Neurological Diseases Foundation (CIEN Foundation) and the Hospital Universitario Fundación Alcorcón, where we plan to confirm its feasibility and usability by a broader clinical test.
Conclusions: We have developed a m-health application, based on Python and Kivy libraries, to view and analyze EEG data on mobile devices. We are also validating this application in simultaneous EEG-fMRI acquisition.
Future developments will include new functionalities and algorithms, and compliance with other formats such as Neuroscan, EDF or KIV. We are also working in creating modules for other electrophysiological data.
The deployment of new communication infrastructures and the use of mobile devices to provide medical services have changed the paradigm of e-health. Wireless networks (WLAN) extend the conventional concept of bedside monitoring to ambulatory monitoring, providing a new paradigm of patient care, whether in hospital or home care.
This situation has led to the proliferation of several software tools to acquire and process different signals. Unfortunately, not all software is able to interact with all types of acquisition hardware. Additionally, most software tools are proprietary or are based on proprietary software.
Objective: to develop a cross-platform m-health application to view and analyze EEG data.
Methods: The application is written in the cross-platform Python programming language and uses the free and open source Kivy libraries for developing multi-touch application software with a natural user interface (NUI). Thus, it runs over Android and iOS as well as in desktop operating systems (Windows, Linux, MacOS X).
Our application can communicate wirelessly with proprietary and non-proprietary standards of different EEG systems. Its main functionality includes both client and server functionality over TCP/IP, to receive retransmitted data from acquisition devices and to allow for peer-to-peer connection respectively.
The application also includes several basic signal processing algorithms, including DC removal and frequency filters. Additionally, it allows to read data from stored files and generate synthetic signals (for testing purposes).
The application is modular; the chosen programming language and libraries provide inherent cross-platform capabilities to ensure portability over different devices. This implementation allows mobility for ubiquitous data access and pervasive healthcare.
Results: We are currently testing our application for different EEG devices, such as the EEG Brain Vision system, and on different Android Smartphones and tablets, such as the Samsung Galaxy S III and the Samsung Galaxy Tab 2, and Ubuntu laptops.
We are also validating the viewer in simultaneous EEG-fMRI (Electroencephalography and functional Magnetic Resonance Imaging) acquisition. The EEG signals and fMRI images are acquired with the EEG Brain Vision system and a GE Signa HDxt 3.0T respectively. We use a Field Programmable Gate Array (FPGA) for real-time artifact (imaging, ballistocardiographic and eye movement artifacts) rejection, based on acquisition information received from the MRI scanner.
These results are being validated at the Research Center for Neurological Diseases Foundation (CIEN Foundation) and the Hospital Universitario Fundación Alcorcón, where we plan to confirm its feasibility and usability by a broader clinical test.
Conclusions: We have developed a m-health application, based on Python and Kivy libraries, to view and analyze EEG data on mobile devices. We are also validating this application in simultaneous EEG-fMRI acquisition.
Future developments will include new functionalities and algorithms, and compliance with other formats such as Neuroscan, EDF or KIV. We are also working in creating modules for other electrophysiological data.
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