By Lun-De Liao, Member IEEE, Chin-Teng Lin, Fellow IEEE, Kaleb McDowell, Senior Member IEEE, Alma E. Wickenden, Member IEEE, Klaus Gramann, Tzyy-Ping Jung, Senior Member IEEE, Li-Wei Ko, Member IEEE, and Jyh-Yeong Chang, Member IEEE
NOTE: This is an abstract of the entire article, which appeared in the special Centennial issue (dated May 13, 2012) of the Proceedings of the IEEE.
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The study of brain-computer interfaces (BCIs) has undergone 30 years of intense development and has grown into a rich and diverse field. BCIs are technologies that enable direct communication between the human brain and external devices. Conventionally, wet electrodes have been employed to obtain unprecedented sensitivity to high temporal resolution brain activity; recently, the growing availability of various sensors that can be used to detect high quality brain signals in a wide range of clinical and everyday environments is being exploited.
This development of biosensing neurotechnologies and the desire to implement them in real world applications have led to the opportunity to develop augmented BCIs (ABCIs) in the upcoming decades. An ABCI is similar to a BCI in that it relies on biosensors that record signals from the brain in everyday environments; the signals are then processed in real time to monitor the behavior of the human. To use an ABCI as a mobile brain imaging technique for everyday, real-life applications, the sensors and the corresponding device must be lightweight and the equipment response time must be short.
This study presents an overview of the wide range of biosensor approaches currently being applied to ABCIs, from their use in the laboratory to their application in clinical and everyday use. The basic principles of each technique are described along with examples of current applications of cutting-edge neuroscience research. In summary, we show that ABCI techniques continue to grow and evolve, incorporating new technologies and advances to address ever more complex and important neuroscience issues, with advancements that are envisioned to lead to a wide range of reallife applications.
The extensively footnoted article explores a wide range of technologies being used to sense brain activity, including magnetic resonance imaging (fMRI), positron emission tomography (PET), electroencephalograms (EEGs) and optical brain imaging techniques (i.e., nearinfrared spectroscopy (NIRS), laser speckle imaging, and functional photoacoustic imaging (fPAM)). Sensor types, including the variety of wet and dry electrodes and new nanotechnology devices, are discussed in detail. Several sections of the article present the state-of-the-art in biosensors and associated instrumentation, along with perspectives on emerging technologies in power sources, mobile imaging, and wearable sensors. The article closes with a view of trends in ABCIs, focusing on current and future applications.
ABOUT THE AUTHORS
L.D. Liao is with the Department of Electrical Engineering and the Brain Research Center at National Chiao Tung University, Hsinchu 300, Taiwan.
C.T. Lin is with the Department of Electrical Engineering, the Brain Research Center, and the CS/EE Departments at National Chiao Tung University, Hsinchu 300, Taiwan (email: email@example.com).
K. McDowell is with the Translational Neuroscience Branch, Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA.
A. E. Wickenden is with the Sensors & Electron Devices Directorate, Army Research Laboratory, Adelphi, MD 20783 USA.
K. Gramann and T.P. Jung are with the Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California at San Diego, La Jolla, CA 92093 USA.
L.W. Ko is with the Brain Research Center and the Department of Biological Science and Technology at National Chiao Tung University, Hsinchu 300, Taiwan.
J.Y. Chang is with the Department of Electrical Engineering, National Chiao Tung University, Hsinchu 300, Taiwan.