Brain-controlled interfaces

Brain-Controlled Interfaces. A two-electrode device was implanted into a 1998 stroke victim who could communicate only by blinking his eyes. The device read from only a few neurons and allowed him to select letters and icons with his brain. A team of researchers helped a young patient with a spinal cord injury by implanting electrodes into his motor cortex that were connected to an interface. The patient was able to use the system to control a computer cursor and move objects using a robotic arm. 

Brain-controlled interfaces rely on four main recording modalities electroencephalography, electro-corticography, local field potentials, and singe-neuron action potentials. The methods are noninvasive, semi-invasive, or invasive, depending on where the transducer is placed scalp, brain surface, or cortical tissue. 

D.R. Kipke, R.J. Vetter, J.C. Williams, and J.F. Hetke, Silicon-substrate intracortical microelectrode arrays for long-term recording of neuronal spike activity in cerebral cortex. In 2nd International Meeting on the Brain-Computer Interfaces for Communication and Control. Rensselaerville, Institute of Electrical and Electronics Engineers Inc., New York, 2002. 

Eleni A. 2008. Control of medical robotics and neurorobotic prosthetics by noninvasive brain-robot interfaces via EEG and RFID technology. 2008 8th IEEE International Conference on Bioinformatics and BioEngineering -4, Athens, Greece. 

Biosignal-brain EEG Enables communication and control with brain signal patterns. Used in the expanding field of brain-computer interfaces... 

Wolpaw, J., Birbaumer, N., McFarland, D., Pfurtscheller, G., and Vaughan, T., 2002. Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791. 

At the forefront of engineering innovation in healthcare are ways of interfacing devices such as prosthetic limbs to the peripheral nervous systems and brain [10], These can, for example, aim to allow movement of a prosthetic limb with subconscious control of a type similar to that of natural limbs. Such developments raise issues regarding the nature and status of the delegated action, which is partly controlled by a microprocessor. Provision of non-invasive brain-computer interfaces to aid patients with degenerative conditions such as amyotrophic lateral syndrome (Tocked-in syndrome ) may require very detailed knowledge of how the brain processes information and how this is affected by the condition. 

Fig. 9. 30. Sophisticated multicomponent control system with adaptronic elements (slip detector, motion sensors for gaze control) for controlling grasping in a paralyzed arm. Brain-Machine-Interfaces are currently under invention for direct thought control...

Patil, P.G. Caxmena, J.M. Nicolelis, M.A. Turner, D.A. Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface. Neurosurgery 55(1) (2004), pp. 27-38... 

Control Brain Machine Interface for a Power Wheelchair... 

Brain-computer interface technology uses the signals from the brain to control external devices. BCI consists of the invasive type and the non-invasive type. The non-invasive type has several advantages, such as its straightforward usability and the ease with which brain signals can be obtained. Non-invasive BCI measures the EEG from the scalp and evaluates the mental activity of human subjects. The point is the connection between the characteristics of EEG and mental activity [1]. 

Electroencephalography (EEG) is a procedure to measure electrical activities by electrodes on scalp. EEG signals analysis have mainly involved with Brain -computer interface (BCI) technology which is a direct technological interface between a brain and a computer not requiring any motor output from the user. BCIs detect electrical activity in the brain indirectly and noninvasively through the scalp via EEG. It provides users with communication channels that do not depend on peripheral nerves and muscles [1] and can provide communication and control to people who are totally paralyzed (e.g., amyotrophic lateral sclerosis (ALS) or brainstem stroke) or have other severe motor disabilities [2] [3]. 

Eleanor A. Curran and Maria J. Stokes (2003) Learning to control brain activity A review of the production and control of EEG components for driving brain-computer interface (BCl) systems. Brain and Cognition. 51 (2003) 326-336... 

Brain-computer interfaces can be used for state-of-the-art medical devices to control and communicate with neural prostheses such as artificial hands, feet and wheelchairs. The importance of BCI neuro-technology places BT-IT-NT-CT convergence technology in the 21th century in a position to have a considerable impact on society. To implement a brain-computer interface system, either a noninvasive or an invasive method can be used. With the invasive method, bio-compatibility, infection and ethical issues arise. These issues make noninvasive methods preferable over invasive methods for commercialization. Noninvasive brain-computer interface systems provide a method of control and communication for those who are severely disabled. These methods also allow the creation of a more intelligent and human friendly user interface for computer software [1]. 

Advances in microelectrode arrays (MEAs) have enabled neuroscientists and researchers in biomedical engineering to take advantage of a large number of channels [22], and this has made it possible to pursue a variety of neuroprosthetic applications such as brain-controlled limb prostheses to treat spinal cord injuries and paralysis. A brain-machine interface (BMI) is at the core of these applications to sense the signals from the brain. 

A multichannel neural-recording system is used in neuroscience experiments to study complex neural networks of animals in their natural environments [130]. It is also a critical component in brain-computer interface used for cortical-controlled neural prosthetics, which has a wide range of applications such as upper and lower limb prostheses [131-134], bladder and bowel movement control for spinal cord injury (SCI) patients [135-136], respiration control for SCI patients [137], and hand grasping function restoration [138]. 

Chapin, J., and M. NicoleUs. 2002. Qosed-Loop Brain-Machine Interfaces. Proceedings of Brain-Computer Interfaces for Communication and Control. Rensselaerville, New York Wadsworth. 

The notion that the brain can be directly accessed to allow a human being to control an external device with thoughts alone is emerging as a real option for patients with motor disabilities. This area of study, known as neuroprosthetics, has sought to create devices, known as brain-computer interfaces (BCIs), that acquire brain signals and translate them to machine commands such that they reflect... 

Carmena, J., M. Lebedev, R. Crist, J. O Doherty, D. Santucci, D. Dimitrov, S. Patil, C. Henriquez, and M. Nicolelis. 2003. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biology l(2) 193-208. 

Felton, E.A., J.A. Wilson, J.C. Williams, and PC. Garell. 2007. Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. Journal of Neurosurgery 106(3) 495-500. 

Leuthardt, E.C., C. Gaona, M. Sharma, N. Szrama, J. Roland, Z. Freudenbeig, J. Solis, J. Breshears, and G. Schalk. 2011. Using the electrocorticograjAic speech network to control a brain-computer interface in humans. Journal of Neural Engineering 8(3) 036004. 

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