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One of the most well-known and utilized tools in meditation and yoga is the practice of self-observation without judgment, or mindfulness. Mindfulness, something once practiced only in more closeted meditation circles, has recently become a greater mainstream interest. However tenuous these preliminary studies are, they are augmented by current neuroscientific research that reveals how mindfulness meditation can significantly change the brain. Mindfulness training has a notable impact on the limbic system, or the emotional system of the brain.
Perhaps even more intriguing, however, is that mindfulness can actually change the size the amygdala. Findings also show that mindfulness practices help the person reduce emotional reactivity by increasing activity in the prefrontal cortex (PFC). Neuroscientific research has also found that mindfulness meditation changes how we experience ourselves in the world. While the cushion is helpful in mindfulness meditation, mindfulness can be practiced at any time and in any situation.
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.
A new resin material that can be molded into complex, highly conductive 3D structures with features just a few microns across has been developed by Tokyo Institute of Technology.
Combined with state-of-the-art micro-sculpting techniques, the new resin holds promise for making customized electrodes for fuel cells or batteries, or biosensor interfaces for medical uses. Though its surface has been turned to carbon, the bunny-like features in the tiny bunny pictured above created by a new process can still be easily observed with a microscope. This nano-rabbit sculpture, the size of a typical bacterium, is one of several whimsical shapes created by a team of Japanese scientists using a new material that can be molded into complex, highly conductive 3D structures with features just a few micrometers across. Combined with state-of-the-art micro-sculpting techniques, the new resin holds promise for making customized electrodes for fuel cells or batteries, as well as biosensor interfaces for medical uses.
Two microstructures made with the new material, containing the lowest concentration of RDGE. Two microstructures made with the new material, containing the highest concentration of RDGE. The work opens a door for researchers trying to create conductive materials in almost any complex shape at the microscopic or cellular level.
These brain interfaces, rows of needle-shaped electrodes pointing in the same direction like teeth on combs, can send or receive electrical signals from neurons and can be used for deep brain stimulation and other therapeutic interventions to treat disorders such as epilepsy, depression, and Parkinson’s disease. Currently, researchers have access to materials that can be used to make complex 3D structures. In this stage, a structure is baked at a temperature high enough to turn its surface to carbon. The Japanese team, led by Daicho and his advisor Shoji Maruo, sought to develop materials that would fit these needs. The researchers also tested their new resin's ability to be manipulated using techniques specifically suited for 3D printing.
Brain computer interfaces (BCI), are systems that use brain signals (electric, magnetic, metabolic) to control external devices such as computers, switches, wheelchairs, or neuroprostheses. Our BCI system has an EEG data acquisition module, signal preprocessing with online blind-source separation (BSS) to reduce artifacts and improvement signal to noise ratio, a features extraction system and an ensemble of adaptive classifiers (Fig. Taking a different approach, we designed a smart multiple choice table in a form of an array of small checkerboard images flickering with different frequency. Although the above paradigms are quite well known, our designs of experiments are quite original.
Another promising extension of BCI is to incorporate various neurofeedbacks to train subjects to modulate EEG brain patterns and parameters such as ERPs, ERD, SMR, P300 or slow cortical potentials (SCPs) to meet a specific criterion or to learn self-regulations skills. 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. Swami Kripalu called self-observation without judgment "the highest form of spiritual practice." Likewise, if you go to any yoga or meditation class you're likely to hear words like mindfulness and nonjudgmental awareness repeated throughout the class.
Perhaps for this reason, research on mindfulness meditation has increased considerably over the last decade. And these changes are not just seen in cave-dwelling monks -- they also occur in average hardworking, child-raising folks -- like most of us.
Specifically, mindfulness meditation has been shown to reduce activity in the amygdala, a region of the brain that determines how much stress we experience and that is central in modulating our fear responses.
One study on overstressed businesspeople found that after eight weeks of mindfulness meditation training, the size of the amygdala actually shrunk compared to those who were not practicing mindfulness.

The PFC, a brain region particular to homo sapiens, which is in charge of activities such as decision-making, planning, abstract thinking, and regulating emotions.
Specifically, a study showed that mindfulness practice increased activity in the PFC such that attention span improved. It teaches us to notice how the body feels, right now, paying attention to the breath and observing, without grasping onto our current state of mind. Through mindfulness practice, activity in the CMS, the part of the brain related to our personal narrative decreased, and activity in the insula, the part of the brain related to subjective awareness and body awareness, increased. Mindfulness is an opportunity for the brain to strengthen and enhance itself -- it's like taking the brain to the gym.
In every moment, we can choose to bring our attention back to the present and to know that when we do, we are actively involved in shaping our brains to foster more peace and inner ease. 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.
But the commercially available resins that work best with modern 3D shaping techniques do not respond to carbonization, a necessary part of the electrode preparation process.
The process of "carbonizing," or charring, increases the conductivity of the resin and also increases its surface area, both of which make it a good electrode. Trained as a chemist, Daicho developed a light-sensitive resin that included a material called Resorcinol Diglycidyl Ether (RDGE), typically used to dilute other resins but never before used in 3D sculpting. In one technique, called microtransfer molding, the light-sensitive liquid was molded into a desired shape and then hardened by exposure to ultraviolet (UV) light. 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. While BCI research hopes to create new communication channels for disabled or elderly persons using their brain signals, recently efforts has been focused on developing potential applications in rehabilitation, multimedia communication and relaxation (such as immersive virtual reality control). Since the EEG are rather weak and are substantially disturbed by on-going activity, artifacts and noise, needed to extract and discriminate useful brain patterns, we need powerful blind signal processing and machine learning algorithms. 2: (Upper) Diagram of one of our developed EEG-BCI classification method for imaginary hands movements.
3: Spectrogram (time-frequency representation) of EEG for SSVEP BCI, Horizontal red segments represent discriminated SSVEPs corresponding to various flicking frequency (10,12,15,20,30 Hz) of images for which a subject was focused on. These oscillations typically decrease during movement or when preparing for movement (event related desynchronization, ERD) and increase after movement and in relaxation (event-related synchronization, ERS). Moreover, we developed and implemented novel and efficient, real-time signal preprocessing tools, and feature extraction algorithms. Hidenori Kimura, Director of RIKEN BSI-TOYOTA Collaboration Center, wins the Giorgio Quazza Medal. Even the National Institutes of Health has grown increasingly more interested in mindfulness meditation, funding a number of large studies which investigate the effects of mindfulness on emotional and physical health outcomes. For example, people with very active amygdalae tend to experience more depression and anxiety. People with post-traumatic stress disorder, for example, have an overactive amygdala and an underactive PFC.
Another study revealed that mindfulness increased activity in the anterior cingulate cortex -- a part of the brain that is closely connected to the PFC and is correlated with empathy and decision-making. We obsess about the future, we dwell on that conversation we had with our spouse last week, or last year, and we remain entrenched in the storyline of our life.
By definition, mindfulness moves us away from our personal narrative about how our life should be and into how life actually is, moment to moment.
Researchers postulate that this may contribute to some of the subjective benefits of mindfulness practice: When we move out of the story of our lives and into the actual lived experience of it, we feel better. From our experience of working with health-care professionals -- some of the most highly stressed individuals in today's workforce -- you don't need to spend hours on a meditation cushion to reap the benefits of these practices.
From this view, a touch of mindfulness practice each day becomes a tremendous investment in our physical, mental, and emotional health. 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. This circular table and bunny models, just slightly larger than a typical bacterium, were formed using two-photon polymerization, the preferred method of 3-D shaping. These pyramid and bunny models did not respond to the preferred method of 3-D shaping, so they were created using an alternative process.
The new mixture had a unique advantage over other compounds – it was a liquid, and therefore potentially suitable for manipulation using the preferred 3D sculpting methods.
The other technique, preferred because of its versatility, made use of the liquid resin's property of solidifying when exposed to a laser beam.
In the preprocessing stage, the time-domain EEG signals are transformed into the time-frequency representation by the Morlet wavelet transform. In the development of a BCI we need to handle two learning systems: The machine should learn to discriminate between different complex patterns of brain activity as accurate as possible and the BCI users should learn via different neurofeedback configurations to modulate their EEG activity and self-regulate or control them. ERD and ERS help identify those EEG features associated with the task of motor imagery EEG classification. By applying BSS and bank of sub-band filters, we showed that it is possible to decompose the SSVEP waveforms in a series of cortical components. For example, we have applied adaptive spatial-temporal filtering and new nonnegative matrix factorization (NMF) technique to extract features (Fig.

In those eight weeks, subjects were actually able to change their brain and, consequently, reduce their stress. As these regions show more activation, subjects tend to report greater emotional stability and less reactivity.
Researchers call this the "default network" and it's dominated by cortical midline structures (CMS). Our participants experience results with just five minutes a day of seated breath-awareness meditation or 10 minutes of mindful chair yoga.
After fixing bugs and securing funding, the researchers expect to start human trials in two or three years. What researchers needed were new materials that could be crafted using 3D printing techniques but that would also survive the charring process. In this process, called two-photon polymerization, researchers used the laser to "draw" a shape onto the liquid resin and build it up layer by layer. In our lab, we want to improve the performance of current BCI systems by focusing on how and when EEG brain signals can trigger consistent computer commands. NMF with the alpha divergence is applied to determine representative basis vectors and associated discriminant features s(t). Paradigm based on SSVEP remains one of the most promising and reliable communication modes for a fast BCI system that could discriminate of a high number of unique commands or symbols, so as to allow autonomous navigation computer screen cursor (or realize a virtual joystick).
4) is an emerging technology for rehabilitation, but we believe is also a new paradigm in neuroscience that might reveal previously unknown brain activities associated with behavior or self-regulated mental states. It is arguably these changes that contribute to many of the benefits reported by current research. While this "default network" has its benefit, when we spend too much time in self-referential thinking, especially if we are caught up in negative thinking, it can lead to poor emotional and behavioral outcomes, including depression and anxiety. Notice that the pyramid and bunny shrink significantly less than those made from the material with a lower concentration of RDGE.
Once the objects were shaped, they were carbonized and viewed with a scanning electron microscope (SEM). We will use that insight to develop BCI technologies that function in real-time and respond to multiple-commands.
A probabilistic, model-based classifier processes NMF-based features as inputs to generate a final decision. The possibility of automated context-awareness as a new interface goes far beyond the standard BCI with simple feedback control.
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.
Just as you wouldn't expect to see much benefit if you went to the gym only once a week, the same is true of mindfulness training. On going research in our lab is attempting to optimize mu-rhythm and SSVEP detection algorithms for high performance, real-time, multi commands BCI.
We hope to develop the next level of BCI system using neurofeedbacks for some selective cognitive phenomena. 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]. To do so, we need to rely increasingly on findings from other disciplines, especially, neuroscience, information technology, biomedical engineering, machine learning, and clinical rehabilitation.
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. 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. 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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