Posts Tagged Electrodes
[Abstract + References] eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation
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[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation
Researchers are resetting the part of the brain that can shift mood
Like seismic sensors planted in quiet ground, hundreds of tiny electrodes rested in the outer layer of the 44-year-old woman’s brain. These sensors, each slightly larger than a sesame seed, had been implanted under her skull to listen for the first rumblings of epileptic seizures.
The electrodes gave researchers unprecedented access to the patient’s brain. With the woman’s permission, scientists at the University of California, San Francisco began using those electrodes to do more than listen; they kicked off tiny electrical earthquakes at different spots in her brain.
Most of the electrical pulses went completely unnoticed by the patient. But researchers finally got the effect they were hunting for by targeting the brain area just behind her eyes. Asked how she felt, the woman answered: “Calmer in my nerves.”
Zapping the same spot in other participants’ brains evoked similar responses: “I feel positive, relaxed,” said a 53-year-old woman. A 60-year-old man described “starting to feel a little more alive, a little more energy.” With stimulation to that one part of the brain, “participants would sit up a little straighter and seem a little bit more alert,” says UCSF neuroscientist Kristin Sellers.
Such positive mood changes in response to light neural jolts, described in the Dec. 17 Current Biology, bring researchers closer to an audacious goal: a device implanted into the brains of severely depressed people to detect a looming crisis coming on and zap the brain out of it.
It sounds farfetched, and it is. The project is “fundamental, pioneering, discovery neuroscience,” says Mark George, a psychiatrist and neurologist at the Medical University of South Carolina in Charleston. George has been studying depression for 30 years. “It’s like sending a spacecraft to the moon.”
|This video shows the location of brain regions involved in emotion processing: the orbitofrontal cortex (green), cingulate (red), insula (purple), hippocampus (yellow) and amygdala (blue). The dots show where electrodes were placed to monitor seizures in patients with epilepsy.|
Still, in the last several years, teams of scientists have made startling amounts of progress, both in their ability to spot the neural signatures that come with a low mood and to change a person’s feelings.
With powerful computational methods, scientists have recently zeroed in on some key features of depressed brains. Those hallmarks include certain types of brain waves in specific locations, like the one just behind and slightly above the eyes. Other researchers are focused on how to correct the faulty brain activity that underlies depression.
A small, implantable device capable of both learning the brain’s language and then tweaking the script when the story gets dark would be an immensely important clinical tool. Of the 16.2 million U.S. adults with severe depression, about a third don’t respond to conventional treatments. “That’s a huge number of people with a very disabling and probably underdiagnosed and underappreciated illness,” says neurologist Vikram Rao, who is working on the UCSF project with Sellers.
A disease of circuits
When George began studying depression decades ago, the field was still haunted by Sigmund Freud, who blamed the disorder on bad parenting and repressed anger. Soon after came the chemical imbalance concept, which held that the brain just needs a dash of the right chemical signal to fix itself. “It was the ‘brain is soup’ model,” George says. Toss in more of the crucial ingredient — serotonin, for instance — and the recipe would sing.
“We have a very different view now,” George says. Thanks to advances in brain imaging, scientists see depression as a disorder of neural circuits — altered connections between important brain regions can tip a person into a depressed state. “We’ve started to define the road map of depression,” George says.
Depression is a disorder, but one that’s tightly linked to emotion. It turns out that emotions span much of the brain. “Emotions are more widespread than we thought,” says cognitive neuroscientist Kevin LaBar. With his colleagues at Duke University, LaBar has used functional MRI scans to find signatures of certain emotions throughout the brain as people are feeling those emotions. He found the wide neural reach of sorrow, for instance, by prompting the emotion with gloomy songs and films.
Functional MRI allows scientists to see the entire scope of a working brain, but that wide view comes with the trade-off of lower resolution. And resolution is what’s needed to precisely and quickly sense — and change — brain activity. Implanting electrodes, like those used in the UCSF project, gives a more nuanced look into select brain areas. Those detailed recordings, taken from people undergoing epilepsy treatment, are what allowed neural engineer Maryam Shanechi to decode the brain’s emotions with precision.
As seven patients spent time in the hospital with electrodes monitoring brain activity, their emotions naturally changed. Every so often, the participants would answer mood-related questions on a tablet computer so that researchers could measure when the patients shifted between emotions. Then Shanechi, of the University of Southern California in Los Angeles, and her colleagues matched the brain activity data to the moods.
The task wasn’t simple. The implanted electrodes recorded an enormous pile of data, much of it irrelevant to mood. Shanechi and her team developed an algorithm to distill all that data into a few key predictive brain regions for each person. The resulting decoder could tell what mood a person was in based on brain activity alone, the team reported in the October Nature Biotechnology. “In every single individual, we can show how their mood changes in real time,” Shanechi says.[…]
[Abstract] Digital mirror box: An interactive hand-motor BMI rehabilitation tool for stroke patients
[Abstract] Design and Test of a Closed-Loop FES System for Supporting Function of the Hemiparetic Hand Based on Automatic Detection using the Microsoft Kinect sensor
Muscle fatigue is a phenomenon associated with the muscle contraction. It is understood as the reduction in the ability of maximal force generation by the muscle with time, during its stressing, as the muscle contraction keeps on increasing. The nervous system’s limitation to generate sustainable signals and the reduction of ability of muscle fiber to contract are two major factors contributing to fatigue development . Fatigue development limits the performance and capability of the individual in sports, long stretch driving conditions and in rigourous day to day activities. Hence a parameter that can estimate the fatigue levels and provide a break point for maximum fatigue can be useful for physiology and in other areas such as labour. People working under mines can be monitored for the fatigue break point and the overall productivity of such areas can be increased by proper analysis. The fatigue development in a person can be analysed via number of methods based on physiological changes. These include Electroencephalogram (EEG), Elec-tromyography(EMG), and Heart Rate Variability(HRV). Zadry et.al.  reported the increase in alpha band power level of EEG with time for fatigue development . Ali et.al. also reported increase in RMS values of different bands in EEG . Few studies measure brain activity in light repetitive task using EEG  to measure drowsiness or fatigue on drivers   and night work  . The EEG analysis for overall fatigue has been the focus of research, but research for specific muscle fatigue detection has been limited. The EEG based detection of fatigue has the advantage of quantitative based assessment. But, for real time application perspective faster computational power and signal processing methods are required. One of the challenges based on EEG based approach is the disturbances and contamination of the signal from eyes blinking action, muscle noise by movements and instrumental noises like line noise, electronic interferences . Another problem is imposed by the inter-variability and intra-variability in EEG dynamics accompanying loss of alertness .
[Abstract] Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback
These following videos show electrode positions to produce:
1. Dorsiflexion with eversion
2. Dorsiflexion with less eversion
3. Balance of 1 and 2
4. Dorsiflexion with least eversion
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