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29.05.2014
In the short term, the device would help patients whose brains can’t drive the vocal machinery in their mouths.
In the long term, the technology underlying this prosthetic could advance the broader field of brain-machine interfaces.
This environment is complicated, but a speech prosthetic isn’t actually as tricky as you might think. Reading brain commands for mouth movements may be simpler than reading cognitive content, but it is hardly easy. Beyond mapping the precise locations of the brain areas controlling these movements for the first time, Chang and colleagues also recorded and analyzed patterns of neuron activity in those areas. The grids stay in place for up to two weeks, as the patient experiences a number of seizures, at which point the grids are removed. Kristofer Bouchard, a postdoctoral fellow in Chang’s lab, then applied state-space analysis, a mathematical technique for understanding the structure of a high-dimensional, complex system like the vSMC. Soon a permanently embedded grid of electrodes will identify those patterns and simultaneously send them to an external processer that will convert the brain signals into synthesized words. Helping voiceless patients speak their minds is already a laudable goal, but this device could do more.
UC San Diego neuroscientist Bradley Voytek suggests such speech-reading brain-machine interfaces (BMIs) could make an excellent control interface for all kinds of devices beyond voice synthesizers because speech is so precise.
New UH research has demonstrated that an amputee can grasp with a bionic hand, powered only by his thoughts.A research team from the University of Houston has created an algorithm that allowed a man to grasp a bottle and other objects with a prosthetic hand, powered only by his thoughts.
The technique, demonstrated with a 56-year-old man whose right hand had been amputated, uses non-invasive brain monitoring, capturing brain activity to determine what parts of the brain are involved in grasping an object. Previous studies involving either surgically implanted electrodes or myoelectric control, which relies upon electrical signals from muscles in the arm, have shown similar success rates, according to the researchers.
Jose Luis Contreras-Vidal, a neuroscientist and engineer at UH, said the non-invasive method offers several advantages: It avoids the risks of surgically implanting electrodes by measuring brain activity via scalp electroencephalogram, or EEG. The results of the study were published March 30 in Frontiers in Neuroscience, in the Neuroprosthetics section. Contreras-Vidal, Hugh Roy and Lillie Cranz Cullen Distinguished Professor of electrical and computer engineering at UH, was lead author of the paper, along with graduate students Harshavardhan Ashok Agashe, Andrew Young Paek and Yuhang Zhang.
The work, funded by the National Science Foundation, demonstrates for the first time EEG-based BMI control of a multi-fingered prosthetic hand for grasping by an amputee.
That provided evidence that the brain predicted the movement, rather than reflecting it, he said.
Until now, this was thought to be possible only with brain signals acquired invasively inside or on the surface of the brain. Researchers first recorded brain activity and hand movement in the able-bodied volunteers as they picked up five objects, each chosen to illustrate a different type of grasp: a soda can, a compact disc, a credit card, a small coin and a screwdriver. They then fitted the amputee subject with a computer-controlled neuroprosthetic hand and told him to observe and imagine himself controlling the hand as it moved and grasped the objects.
The subject’s EEG data, along with information about prosthetic hand movements gleaned from the able-bodied volunteers, were used to build the algorithm.
Contreras-Vidal said additional practice, along with refining the algorithm, could increase the success rate to 100 percent. Controlling machines with the human brain the way brain controls the human body has been the stuff of science fiction for decades. A comprehensive guide of the top 93 technology conferences to attend in 2016, covering DevOps, cloud, IT ops, agile, app dev, security, mobile, IoT, testing, QA, and more.
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Correlation between EEG?EMG coherence during isometric contraction and its imaginary execution. Modulation of mu rhythm desynchronization during motor imagery by transcranial direct current stimulation. Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy. Between-subject variance in the magnitude of corticomuscular coherence during tonic isometric contraction of the tibialis anterior muscle in healthy young adults. Effects of Neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a case series study. That's because the trickiest part — creating a reliable brain-machine interface (BMI) — is a tough job, since we don't fully understand how the brain works. Their research aims to figure out what people with paralysis or brain injury are trying to say by studying how they attempt to move their mouths. That includes the thousands of people with brain trauma, spinal cord injury, stroke or ALS who are fully conscious but unable to express their thoughts.
The key to this device is not so much in its physical mechanisms but in the algorithms behind it, says neurosurgeon Edward Chang of the University of California, San Francisco. The analysis allowed the researchers to identify and isolate the specific patterns of neural activity that play out in this part of the brain when someone speaks, effectively enabling them to decode the brain’s signals. The technology may one day let healthy people control electronic gadgets with their thoughts.
We have much better control over what we say (even what we say to ourselves in our own minds) than over what we think. This technology already promises to make a difference for patients who’ve lost their voices. With that information, researchers created a computer program, or brain-machine interface (BMI), that harnessed the subject’s intentions and allowed him to successfully grasp objects, including a water bottle and a credit card.
And myoelectric systems aren’t an option for all people, because they require that neural activity from muscles relevant to hand grasping remain intact. Contreras-Vidal said brain activity was recorded in multiple areas, including the motor cortex and areas known to be used in action observation and decision-making, and occurred between 50 milliseconds and 90 milliseconds before the hand began to grasp. The recorded data were used to create decoders of neural activity into motor signals, which successfully reconstructed the grasping movements. But recent research in the medical field and the arrival of virtual reality hardware in the gaming realm have brought the technology closer to reality. Even a simple phrase is the equivalent of an Olympic gymnastics routine for the speaker’s tongue, lips, jaw and larynx.
To find out how the human brain coordinates speech, the researchers had to study it in people. For commercial speech-reading BMIs to become mainstream, one of two things would need to happen: Brain implant surgery would have to become much safer, cheaper and more routine, or noninvasive sensing devices would have to become much more powerful. The subject grasped the selected objects 80 percent of the time using a high-tech bionic hand fitted to the amputee’s stump. After fixing bugs and securing funding, the researchers expect to start human trials in two or three years. For example, when the brain senses the temperature in a room is too cold, an app could automatically turn up a smart thermostat.Before the marriage of brains and machines can make it to a broad audience of developers, the technology needs to be perfected. Medicine is a good field in which to do that, which is why MindMaze began developing its BMI there.


In the future, neuroprosthetics must move as quickly as natural limbs through three dimensions in natural environments.Brain-machine interactive control (BMIC) of prosthetic limbs for high speed and natural movements is a major challenge. The current BMIC paradigm employs a feedforward interface between the brain and (artificial) limb, often referred to as the “decoder”, whose success relies heavily on the ability of the brain to adapt appropriately utilizing visual feedback information in a “certain” environment [1]-[8]. Such decoders are typically trained using data from healthy subjects but are eventually implemented as interfaces for amputees or for patients with spinal cord injuries.
The motor cortical output of the healthy subject is substantially different from that of an injured patient, and decoders do not account for spurious signals generated in the cerebellum due to the loss of proprioceptive data (see Fig.
MindLeap marks the quickest reaction time to date, with near-millisecond synchronization, the company says.
Before year's end, the company expects to have its neuro engine available to game developers via a software development kit (SDK).
It has been shown that neural signals can be used in a feedforward decoder to predict repeatable low speed movements. Here, the decoder performs well because the motor cortical output of the healthy subject and the injured patient are very similar.
However, the loss of proprioceptive feedback is detrimental when executing fast movements in uncertain environments.
The key challenge facing the field is to account for cerebellar inputs to achieve advanced features of high-speed and loaded movements.
Thus, we need to design robust decoders for BMICs of the future that take into account both cerebellar and cortical contributions and to address the realistic control of prosthetics faced by the injured or diseased human subjects.To address this challenge, We will work towards developing a novel model-based Robust Decoder-Compensator (RDC) architecture for interactive control of fast movements in the presence of uncertainty.
The RDC is a feedback interconnection that 1) decodes cortical signals to produce actuator commands that reflect motor intent, 2) corrects for spurious signals generated by the cerebellum in the absence of proprioceptive feedback, and 3) makes robust the interconnection to account for mismatches between models and reality (Fig.
If sufficiently reduced-order models of the limb, prosthetic and CerebroCerebellar processing are known, and if the architecture for the RDC is known, then the RDC can be designed to minimize the error and this optimization problem can be solved either exactly or approximately.To carry out this ambitious project, We will have the unique opportunity to work with clinicians at the Cleveland Clinic (CC) with expertise in electrophysiology and neurosurgery. It uses an articulated steel frame designed to move as a human within it moves.Once BMI moves beyond sharing control of objects, the technology could move into learning, which will open it up to educational developers.
During recordings of cerebral motor and premotor areas, the patients will perform a behavioral task involving a manipulandum (robotic arm). Patients will attempt to move the manipulandum to targets as quickly as possible while the robotic arm may perturb or resist the patient’s motion. This data will then be used to estimate neuroanatomically-based models of the cerebellum (extending work in [9]-[11]) and linear parameter varying (LPV) phenomenological models of motor sensory areas. These models will be incorporated in the RDC, and their predictions will be used to compensate for the effects of spurious signals generated by these regions which no longer receive proprioceptive feedback.Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, and Donoghue JP (2006). Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex, Nat Neurosci, vol.
Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans, Proc Natl Acad Sci U S A, vol. Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand. 40th Annual meeting of Society for Neuroscience.Aggarwal V, Acharya S, Tenore F, Shin HC, Etienne-Cummings R, Schieber MH, and Thakor NV (2008).
Asynchronous decoding of dexterous finger movements using M1 neurons, IEEE Trans Neural Syst Rehabil Eng, vol.
2004 91(3):188-202Jo S, and Massaquoi, SG (2006) A model of cerebrocerebellar-spinomuscular interaction in the sagittal control of locomotion” Biol.



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