Abstract
EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.
Highlights
In recent years, electroencephalography (EEG) based brain computer interfaces (BCI) have left their laboratory cradles and began to seek real-world applications (Lance et al, 2012)
Potential cost/benefit tradeoffs are considered. Since this is an on-going work to develop a pilot system, a list of future work is provided at the conclusion. This pervasive on-line EEG-BCI system was built upon two information and communication technologies: (1) a multi-tier distributed computing infrastructure that is based on Fog and Cloud Computing paradigms and (2) a semantic Linked Data superstructure that connects all the data entries maintaining in this distributed computing infrastructure through meta-data annotation
In order for our EEG-BCI system to work with several EEG analysis MATLAB® toolboxes including (BCI2000, 2014; BCILAB, 2014; EEGLAB, 2014), we developed an application program interface (API) between the MQTT publish/subscribe data transport protocol and the MATLAB toolboxes using the Lab Streaming Layer (LSL) middleware (Kothe, 2014a)
Summary
Electroencephalography (EEG) based brain computer interfaces (BCI) have left their laboratory cradles and began to seek real-world applications (Lance et al, 2012). Wearable BCI headsets such as Emotiv EPOC, NeuroSky MindSet and MINDO are selling as consumer products while applications such as silent communication using The Audeo by Ambient and focus/relax exercises using the Mindball by Interactive Productline are attracting widespread attention. Despite this hype, BCI applications still need to overcome a few basic challenges in order to become truly useful in real-world settings: 1. There is a pressing need to identify common EEG correlates of certain brain states in order to reduce the amount of training data required to calibrate individual users’ BCI systems
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