Caroline Hartley and Oxford colleagues studied 72 infants during painful medical procedures. Using EEG, they found a signature change in brain activity about a half-second after a painful stimulus. They seek to understand its use in monitoring and managing infant pain, as well as the use of EEG in adult pain treatment.
EEG is more precise than current heart rate, oxygen saturation level, and facial expression pain assessment, which are affected by other stressful, non-painful events.
In one experiment, 11 out of 12 infants had a decreased pain-related EEG signal after doctors applied a topical anesthetic to their feet. A new study uses EEG to test the efficacy of morphine in infants, whose skin and intestines absorb drugs differently than adults.
EEG is being miniaturized by companies such as Neurosteer, making it an increasingly viable option for continuous pain, attention, and consciousness monitoring and treatment optimization.
Join ApplySci at Wearable Tech + Digital Health + NeuroTech Boston on September 19, 2017 at the MIT Media Lab – featuring Joi Ito – Ed Boyden – Roz Picard – George Church – Nathan Intrator – Tom Insel – John Rogers – Jamshid Ghajar – Phillip Alvelda – Michael Weintraub – Nancy Brown – Steve Kraus – Bill Geary
After scanning the brains of ALS, epilepsy, minimally conscious, schizophrenia, memory impaired, and healthy patients, to monitor brain health and treatment effectiveness, Brown and Tel Aviv University professor Nathan Intrator has commercialized his algorithms and launched Neurosteer.
At ApplySci’s recent NeuroTech NYC conference, Professor Intrator discussed dramatic advances in BCI, monitoring, and neurofeedback, in his keynote, and in a panel with DARPA’s Biological Technologies Office Director Justin Sanchez. He also described Neurosteer’s progress — and the rapidly developing world of technology for cognitive and emotional wellness — in an interview with StartUp Health’s Unity Stoakes. Click to view the June, 2016 StartUp Health NOW interview, or Steve Krein and Intrator’s June, 2015 interview here.
ApplySci’s 6th Wearable Tech + Digital Health + NeuroTech Silicon Valley – February 7-8 2017 @ Stanford | Featuring: Vinod Khosla – Tom Insel – Zhenan Bao – Phillip Alvelda – Nathan Intrator – John Rogers – Mary Lou Jepsen – Vivek Wadhwa – Miguel Nicolelis – Roozbeh Ghaffari – Unity Stoakes – Mounir Zok
Binghamton researchers have developed an EEG “brainprint” system that can identify people with 100 per cent accuracy, according to a recent study.
A brain-password is recorded when a user’s stimulus response activity is recorded via EEG. Identity is then confirmed by exposing the user to the same stimulus, recording their response, and using a pattern classification system to compare the results.
ApplySci described the team’s initial “brain as password” work in 2015, and similar research done at UC Berkeley in 2013.
Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences
NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences
Binghamton professors Sarah Laszlo and Zhanpeng Jin believe that they can verify a person’s identity by using EEG to monitor the way brains respond to words. Their Neurocomputing paper puts forth the view that thoughts can replace passwords.
In April, 2013, ApplySci described a similar study by Berkeley‘s John Chuang.
The researchers observed brain signals of 45 volunteers as they read a list of 75 acronyms. They recorded the brain’s reaction to each group of letters, focusing on the part of the brain associated with reading and recognizing words. Participants’ brains reacted differently to each acronym, and a computer was able to identify each volunteer with 94 percent accuracy. Laszlo and Jin believe that the results show that brainwaves could be used by security systems to verify identity. They further suggest that this method is more secure than fingerprints or retinal patterns in the eye.
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University of Illinois professor John Rogers has made another breakthrough in flexible medical electronics. His team has created an EEG system that sticks to the skin behind one’s ear to monitor brain activity. The miniature, lightweight, gold electrode device sticks to the skin without adhesive, and can be worn continuously for 2 weeks.
While not yet precise or fast enough to replace traditional EEG, study participants were able to spell the word “computer” on a screen using their brain’s electrical activity.
Rogers is now concentrating on refining the device for medical applications, and making it wireless. In a related Neuron paper, he describes advances in soft electronic interface technologies for neuroscience research.
Wearable Tech + Digital Health NYC 2015 – June 30 @ the New York Academy of Sciences, features Professor Rogers’ MC10 colleague, Roozbeh Ghaffari, as a keynote speaker.
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The MICHELANGELO project creates home-based solutions for assessing and treating autism, including:
- Pervasive, sensor-based technologies to perform physiological measurements such as heart rate, sweat index and body temperature
- Camera-based systems to monitor observable behaviors and record brain responses to natural environment stimuli
- Algorithms allowing for the characterization of stimulus-specific brainwave anomalies
These technologies will allow for more intense, personalized treatment, and give parents the role of co-therapist.
Wearable Tech + Digital Health NYC 2015 – June 30 @ New York Academy of Sciences. Early registration rate ends Friday, 4/24.
MIT‘s John Gabrieli is investigating the use of neuroimaging to predict future behavior to customize brain health treatments.
Professor Gabrieli believes that neuromarkers, determined by fMRI, can be used to develop personalized interventions to improve education, health, addiction, criminal behavior and to analyze responses to drug or behavioral treatments.
According to Gabrieli, “Presently, we often wait for failure, in school or in mental health, to prompt attempts to help, but by then a lot of harm has occurred. If we can use neuroimaging to identify individuals at high risk for future failure, we may be able to help those individuals avoid such failure altogether.”
The cost of fMRI could pose a challenge for implementation. Cheaper, quicker, mobile EEG solutions could complement this research, and help bring imaging to the forefront of treatment.
Join ApplySci at Wearable Tech + Digital Health NYC 2015 – June 30 @ New York Academy of Sciences.
Samsung’s Early Detection Sensor & Algorithm Package (EDSAP), developed by Se-hoon Lim, is meant to detect early signs of stroke.
A multiple sensor headset records electrical impulses in the brain, algorithms determine the likelihood of a stroke in one minute, and results are presented in a mobile app. EDSAP can also analyze stress and sleep patterns, and potentially be used to monitor heart activity. The company believes that the system can one day be built into one’s own glasses.
Wearable Tech + Digital Health NYC 2015 – June 30 @ New York Academy of Sciences
Albert Einstein College of Medicine professor Sophie Molholm has published a paper describing the way that autistic children process sensory information, as determined by EEG. She believes that this could lead to earlier diagnosis (before symptoms of social and developmental delays emerge), hence earlier treatment, which might reduce the condition’s symptoms.
EEG readings were taken from 40 children, ages 6-17, who were diagnosed with autism, and compared to those of unaffected children of similar age. All were given either a flash cue, a beep cue or a combination, and asked to press a button when these stimuli occurred. A 70 electrode cap measured brain responses every two milliseconds, including those that recorded how the brain first processed the information.
The children with autism showed a distinctly different brain wave signature from those without the condition. There were differences in the speed in which the sights or sounds were processed, and in how the sensory neurons recruited neurons in other areas of the brain to register and understand the information. The more different this multi-processing was, the more severe the child’s autistic symptoms.
Professor Molholm acknowledges that the sample was too small to use the profile for diagnosing autism, but it could lead to such a test if the results are confirmed and repeated.