Brain Computer Interface

Brain Interface

A brain–computer interface (BCI), sometimes called a mind-machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between the brainand an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.

Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature.

The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.^[3] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s

History

The history of brain–computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity of the human brain and the development of electroencephalography (EEG). In 1924 Berger was the first to record human brain activity by means of EEG. Berger was able to identify oscillatory activity in the brain by analyzing EEG traces. One wave he identified was the alpha wave (8–13 Hz), also known as Berger's wave.

Berger's first recording device was very rudimentary. He inserted silver wires under the scalps of his patients. These were later replaced by silver foils attached to the patients' head by rubber bandages. Berger connected these sensors to a Lippmann capillary electrometer, with disappointing results. More sophisticated measuring devices, such as theSiemens double-coil recording galvanometer, which displayed electric voltages as small as one ten thousandth of a volt, led to success.

Berger analyzed the interrelation of alternations in his EEG wave diagrams with brain diseases. EEGs permitted completely new possibilities for the research of human brain activities.

Professor Jacques Vidal coined the term "BCI" and produced the first peer-reviewed publications on this topic ^[1][2]Vidal is widely recognized as the inventor of BCIs in the BCI community, as reflected in numerous peer-reviewed articles reviewing and discussing the field (e.g.,^[4][5][6]).

Prof. Vidal's first BCI relied on visual evoked potentials, and allows users to control cursor direction. Visual evoked potentials are still widely used in BCIs^{[citation needed]} (Allison et al., 2010, 2012; Bin et al., 2011; Guger et al., 2012; Kaufmann et al., 2012; Jin et al., 2014; Kapeller et al., 2015).^{[clarification needed]}

After his early contributions, Vidal was not active in BCI research, nor BCI events such as conferences, for many years. In 2011, however, he gave a lecture in Graz, Austria, supported by the Future BNCI project, presenting the first BCI, which earned a standing ovation. Vidal was joined by his wife, Laryce Vidal, who previously worked with him at UCLA on his first BCI project. Prof. Vidal will also present a lecture on his early BCI work at the Sixth Annual BCI Meeting, scheduled for May–June 2016 at Asilomar, California.

BCI versus neuroprosthetics

Neuroprosthetics is an area of neuroscience concerned with neural prostheses. That is, using artificial devices to replace the function of impaired nervous systems and brain related problems, or of sensory organs. The most widely used neuroprosthetic device is the cochlear implant which, as of December 2010, had been implanted in approximately 220,000 people worldwide.^[7] There are also several neuroprosthetic devices that aim to restore vision, including retinal implants.

The difference between BCIs and neuroprosthetics is mostly in how the terms are used: neuroprosthetics typically connect the nervous system to a device, whereas BCIs usually connect the brain (or nervous system) with a computer system. Practical neuroprosthetics can be linked to any part of the nervous system—for example, peripheral nerves—while the term "BCI" usually designates a narrower class of systems which interface with the central nervous system.

The terms are sometimes, however, used interchangeably. Neuroprosthetics and BCIs seek to achieve the same aims, such as restoring sight, hearing, movement, ability to communicate, and even cognitive function. Both use similar experimental methods and surgical techniques.

Animal BCI research

Several laboratories have managed to record signals from monkey and rat cerebral cortices to operate BCIs to produce movement. Monkeys have navigated computer cursors on screen and commanded robotic arms to perform simple tasks simply by thinking about the task and seeing the visual feedback, but without any motor output.^[8] In May 2008 photographs that showed a monkey at the University of Pittsburgh Medical Center operating a robotic arm by thinking were published in a number of well known science journals and magazines.^[9] Other research on cats has decoded their neural visual signals.

Early work

In 1969 the operant conditioning studies of Fetz and colleagues, at the Regional Primate Research Center and Department of Physiology and Biophysics, University of Washington School of Medicine in Seattle, showed for the first time that monkeys could learn to control the deflection of abiofeedback meter arm with neural activity.^[10] Similar work in the 1970s established that monkeys could quickly learn to voluntarily control the firing rates of individual and multiple neurons in the primary motor cortex if they were rewarded for generating appropriate patterns of neural activity.^[11]

Studies that developed algorithms to reconstruct movements from motor cortex neurons, which control movement, date back to the 1970s. In the 1980s, Apostolos Georgopoulos at Johns Hopkins University found a mathematical relationship between the electrical responses of single motor cortex neurons in rhesus macaque monkeys and the direction in which they moved their arms (based on a cosine function). He also found that dispersed groups of neurons, in different areas of the monkey's brains, collectively controlled motor commands, but was able to record the firings of neurons in only one area at a time, because of the technical limitations imposed by his equipment.^[12]

There has been rapid development in BCIs since the mid-1990s.^[13] Several groups have been able to capture complex brain motor cortex signals by recording from neural ensembles (groups of neurons) and using these to control external devices.

The BCI Award

The Annual BCI Research Award, endowed with 3,000 USD, is awarded in recognition of outstanding and innovative research in the field of Brain-Computer Interfaces. Each year, a renowned research laboratory is asked to judge the submitted projects and to award the prize. The jury consists of world-leading BCI experts recruited by the awarding laboratory. Following list consists the winners of the BCI Award:

• 2010: Cuntai Guan, Kai Keng Ang, Karen Sui Geok Chua and Beng Ti Ang, (A*STAR, Singapore)

Motor imagery-based Brain-Computer Interface robotic rehabilitation for stroke.

• 2011: Moritz Grosse-Wentrup and Bernhard Schölkopf, (Max Planck Institute for Intelligent Systems, Germany)

What are the neuro-physiological causes of performance variations in brain-computer interfacing?

• 2012: Surjo R. Soekadar and Niels Birbaumer, (Applied Neurotechnology Lab, University Hospital Tübingen and Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls University, Tübingen, Germany)

Improving Efficacy of Ipsilesional Brain-Computer Interface Training in Neurorehabilitation of Chronic Stroke.

• 2013: M. C. Dadarlat^a,b, J. E. O’Doherty^a, P. N. Sabes^a,b (^aDepartment of Physiology, Center for Integrative Neuroscience, San Francisco, CA, US, ^bUC Berkeley-UCSF Bioengineering Graduate Program, University of California, San Francisco, CA, US),

A learning-based approach to artificial sensory feedback: intracortical microstimulation replaces and augments vision.

• 2014: Katsuhiko Hamada, Hiromu Mori, Hiroyuki Shinoda, Tomasz M. Rutkowski, (The University of Tokyo, JP, Life Science Center of TARA, University of Tsukuba, JP, RIKEN Brain Science Institute, JP),

Airborne Ultrasonic Tactile Display BCI