Posts Tagged Electrodes

[Abstract + References] eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation

Abstract

Brain-Computer Interface (BCI) combined with assistive robots has been developed as a promising method for stroke rehabilitation. However, most of the current studies are based on complex system setup, expensive and bulky devices. In this work, we designed a wearable Electroencephalography(EEG)-based BCI system for hand function rehabilitation of the stroke. The system consists of a customized EEG cap, a small-sized commercial amplifer and a lightweight hand exoskeleton. In addition, visualized interface was designed for easy use. Six healthy subjects and two stroke patients were recruited to validate the safety and effectiveness of our proposed system. Up to 79.38% averaged online BCI classification accuracy was achieved. This study is a proof of concept, suggesting potential clinical applications in outpatient environments.

2. E. Donchin , K. Spencer and R. Wijesinghe , “The mental prosthesis: assessing the speed of a P300-based brain-computer interface”, IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 174-179, 2000.

3. D. McFarland and J. Wolpaw , “Brain-Computer Interface Operation of Robotic and Prosthetic Devices”, Computer, vol. 41, no. 10, pp. 52-56, 2008.

4. Xiaorong Gao , Dingfeng Xu , Ming Cheng and Shangkai Gao , “A bci-based environmental controller for the motion-disabled”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 137-140, 2003.

5. A. Ramos-Murguialday , D. Broetz , M. Rea et al “Brain-machine interface in chronic stroke rehabilitation: A controlled study”, Annals of Neurology, vol. 74, no. 1, pp. 100-108, 2013.

6. F. Pichiorri , G. Morone , M. Petti et al “Brain-computer interface boosts motor imagery practice during stroke recovery”, Annals of Neurology, vol. 77, no. 5, pp. 851-865, 2015.

7. M. A. Cervera , S. R. Soekadar , J. Ushiba et al “Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis”, Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651-663, 2018.

8. K. Ang , K. Chua , K. Phua et al “A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke”, Clinical EEG and Neuroscience, vol. 46, no. 4, pp. 310-320, 2014.

9. N. Bhagat , A. Venkatakrishnan , B. Abibullaev et al “Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors”, Frontiers in Neuroscience, vol. 10, pp. 122, 2016.

10. J. Webb , Z. G. Xiao , K. P. Aschenbrenner , G. Herrnstadt , and C. Menon , “Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface”, in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference, pp. 1299-1304, 2012.

11. A. L. Coffey , D. J. Leamy , and T. E. Ward , “A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation”, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 3622-3625, 2014.

12. D. Bundy , L. Souders , K. Baranyai et al “Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors”, Stroke, vol. 48, no. 7, pp. 1908-1915, 2017.

13. X. Shu , S. Chen , L. Yao et al “Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients”, Frontiers in Neuroscience, vol. 12, pp. 93, 2018.

14. A. Delorme , T. Mullen , C. Kothe et al “EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing”, Computational Intelligence and Neuroscience, vol. 2011, pp. 1-12, 2011.

15. G. Schalk , D. McFarland , T. Hinterberger , N. Birbaumer and J. Wolpaw , “BCI2000: A General-Purpose Brain-Computer Interface (BCI) System”, IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034-1043, 2004.

16. M. H. B. Azhar , A. Casey , and M. Sakel , “A cost-effective BCI assisted technology framework for neurorehabilitation”, The Seventh International Conference on Global Health Challenges, 18th-22nd November, 2018. (In Press)

17. C. M. McCrimmon , M. Wang , L. S. Lopes et al “A small, portable, battery-powered brain-computer interface system for motor rehabilitation”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2776-2779, 2016.

18. J. Meng , B. Edelman , J. Olsoe et al “A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance”, Frontiers in Neuroscience, vol. 12, pp. 227, 2018.

19. T. Mullen , C. Kothe , Y. Chi et al “Real-time neuroimaging and cognitive monitoring using wearable dry EEG”, IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2553-2567, 2015.

 

via eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation – IEEE Conference Publication

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[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation

Abstract

Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users [1]. To address this issue, we assess the feasibility of recognizing two self-paced movement intentions of the right upper limb plus a rest state from EEG signals recorded during robot-assisted rehabilitation therapy. In addition, the work proposes the use of Multi-CSP features and deep learning classifiers to recognize movement intentions of the same limb. The results showed performance peaked greater at (80%) using a novel classification models implemented in a multiclass classification scenario. On the basis of these results, the decoding of the movement intention could potentially be used to develop more natural and intuitive robot assisted neurorehabilitation therapies
1. S. R. Soekadar , N. Birbaumer , M. W. Slutzky , and L. G. Cohen , “Brain machine interfaces in neurorehabilitation of stroke,” Neurobiology of Disease, vol. 83, pp. 172-179, 2015.

2. P. Ofner , A. Schwarz , J. Pereira , and G. R. Müller-Putz , “Upper limb movements can be decoded from the time-domain of low-frequency EEG,” PLoS One, vol. 12, no. 8, p. e0182578, Aug 2017, poNE-D- 17-04785[PII].

3. F. Shiman , E. Lopez-Larraz , A. Sarasola-Sanz , N. Irastorza-Landa , M. Spler , N. Birbaumer , and A. Ramos-Murguialday , “Classification of different reaching movements from the same limb using EEG,” Journal of Neural Engineering, vol. 14, no. 4, p. 046018, 2017.

4. J. Pereira , A. I. Sburlea , and G. R. Müller-Putz , “EEG patterns of self- paced movement imaginations towards externally-cued and internally- selected targets,” Scientific Reports, vol. 8, no. 1, p. 13394, 2018.

5. R. Vega , T. Sajed , K. W. Mathewson , K. Khare , P. M. Pilarski , R. Greiner , G. Sanchez-Ante , and J. M. Antelis , “Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals,” Artif. Intell. Research, vol. 6, no. 1, p. 37, 2017.

6. I. Figueroa-Garcia et al , “Platform for the study of virtual task- oriented motion and its evaluation by EEG and EMG biopotentials,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2014, pp. 1174–1177.

7. B. Graimann and G. Pfurtscheller , “Quantification and visualization of event-related changes in oscillatory brain activity in the timefrequency domain,” in Event-Related Dynamics of Brain Oscillations, ser. Progress in Brain Research, C. Neuper and W. Klimesch , Eds. Elsevier, 2006, vol. 159, pp. 79 – 97.

8. G. Pfurtscheller and F. L. da Silva , “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, vol. 110, no. 11, pp. 1842 – 1857, 1999.

9. G. Dornhege , B. Blankertz , G. Curio , and K. Muller , “Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 993–1002, 2004.

10. X. Yong and C. Menon , “EEG classification of different imaginary movements within the same limb,” PLOS ONE, vol. 10, no. 4, pp. 1–24, 04 2015.

11. L. G. Hernandez , O. M. Mozos , J. M. Ferrandez , and J. M. Antelis , “EEG-based detection of braking intention under different car driving conditions,” Frontiers in Neuroinformatics, vol. 12, p. 29, 2018. [Online]. Available: https://www.frontiersin.org/article/10.3389/fninf.2018.00029

12. L. G. Hernandez and J. M. Antelis , “A comparison of deep neural network algorithms for recognition of EEG motor imagery signals,” in Pattern Recognition, 2018, pp. 126–134.

13. M. Abadi et al , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/

via Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation – IEEE Conference Publication

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[NEWS] Brain-zapping implants that fight depression inch closer to reality | Science News

Researchers are resetting the part of the brain that can shift mood

BY LAURA SANDERS, FEBRUARY 10, 2019
neural activity

MOOD CHANGER  Neural activity in certain areas of the brain (brightly colored strands show connections emanating from those regions) can be measured to decode 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.

Some electrical arrays that researchers at the University of California, San Francisco are testing sit on the surface of the brain (top); others penetrate deep into brain tissue (bottom).

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.[…]

more —-> Brain-zapping implants that fight depression inch closer to reality | Science News

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[Abstract] EMG Feature Extractions for Upper-Limb Functional Movement During Rehabilitation

Abstract

Rehabilitation is important treatment for post stroke patient to regain their muscle strength and motor coordination as well as to retrain their nervous system. Electromyography (EMG) has been used by researcher to enhance conventional rehabilitation method as a tool to monitor muscle electrical activity however EMG signal is very stochastic in nature and contains some noise. Special technique is yet to be researched in processing EMG signal to make it useful and effective both to researcher and to patient in general. Feature extraction is among the signal processing technique involved and the best method for specific EMG study needs to be applied. In this works, nine feature extractions techniques are applied to EMG signals recorder from subjects performing upper limb rehabilitation activity based on suggested movement sequence pattern. Three healthy subjects perform the experiment with three trials each and EMG data were recorded from their bicep and deltoid muscle. The applied features for every trials of each subject were analyzed statistically using student T-Test their significant of p-value. The results were then totaled up and compared between the nine features applied and Auto Regressive coefficient (AR) present the best result and consistent with each subjects’ data. This feature will be used later in our future research work of Upper-limb Virtual Reality Rehabilitation.

via EMG Feature Extractions for Upper-Limb Functional Movement During Rehabilitation – IEEE Conference Publication

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[Abstract] A survey on sEMG control strategies of wearable hand exoskeleton for rehabilitation

Abstract:

Surface electromyographic (sEMG) signals is one most commonly used control source of exoskeleton for hand rehabilitation. Due to the characteristics of non-invasive, convenient collection and safety, sEMG can conform to the particularity of hemiplegic patients’ physiological state and directly reflect human’s neuromuscular activity. By way of collecting, analyzing and processing, sEMG signals corresponding to identify the target movement model would be translated into robot movement control instructions and input into hand rehabilitation exoskeleton controller. Then patients’ hand can be directed to achieve the realization of the similar action finally. In this paper, the recent key technologies of sEMG-based control for hand rehabilitation robots are reviewed. Then a summarization of controlling technology principle and methods of sEMG signal processing employed by the hand rehabilitation exoskeletons is presented. Finally suitable processing methods of multi-channel sEMG signals for the controlling of hand rehabilitation exoskeleton are put forward tentatively and the practical application in hand exoskeleton control is commented also.

Source: A survey on sEMG control strategies of wearable hand exoskeleton for rehabilitation – IEEE Xplore Document

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[Abstract] Digital mirror box: An interactive hand-motor BMI rehabilitation tool for stroke patients

Abstract:

We develop a brain-machine interface for the hand-motor rehabilitation of stroke patients. The interface provides both visual and proprioceptive feedback to the user based upon the successful generation of cortical motor commands. We discuss the details of the proposed system and provide a summary of the preliminary experiment. The experiment investigates the importance of simultaneous visual and proprioceptive feedback to the delivery of motor commands from the affected motor cortex of the patients. We also discuss a case study involving a chronic stroke patient who trained with the system for 14 days to recover functional movement in the hand. The results obtained by this study suggest that the developed system is effective at accelerating the recovery of motor function in stroke patients with hand paralysis.

Date of Conference: 13-16 Dec. 2016

Date Added to IEEE Xplore: 19 January 2017

ISBN Information:

Electronic ISBN: 978-9-8814-7682-1

Print on Demand(PoD) ISBN: 978-1-5090-2401-8

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Publisher: IEEE

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Source: Digital mirror box: An interactive hand-motor BMI rehabilitation tool for stroke patients – IEEE Xplore Document

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[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

Abstract:

This paper describes the design of a FES system automatically controlled in a closed loop using a Microsoft Kinect sensor, for assisting both cylindrical grasping and hand opening. The feasibility of the system was evaluated in real-time in stroke patients with hand function deficits. A hand function exercise was designed in which the subjects performed an arm and hand exercise in sitting position. The subject had to grasp one of two differently sized cylindrical objects and move it forward or backwards in the sagittal plane. This exercise was performed with each cylinder with and without FES support. Results showed that the stroke patients were able to perform up to 29% more successful grasps when they were assisted by FES. Moreover, the hand grasp-and-hold and hold-and-release durations were shorter for the smaller of the two cylinders. FES was appropriately timed in more than 95% of all trials indicating successful closed loop FES control. Future studies should incorporate options for assisting forward reaching in order to target a larger group of stroke patients.

Source: 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 – IEEE Xplore Document

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[Abstract] Fatigue detection and estimation using auto-regression analysis in EEG

Abstract:

Estimation of fatigue is a required criteria in the field of physiology. The estimation of muscle fatigue and its development in the brain signals can provide a level of endurance among athletes and limits of a persons in doing physical tasks. In this paper a technique for detecting and estimating the fatigue development using regression parameters for EEG signals is discussed. The study of 14 subjects was undertaken and analysed for the fatigue development using Auto-Regression(AR) model. The behaviour of the error function obtained is analysed for the prediction of the stages and limits of muscle fatigue development.

I. Introduction

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 [1]. 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. [2] reported the increase in alpha band power level of EEG with time for fatigue development [3]. Ali et.al. also reported increase in RMS values of different bands in EEG [4]. Few studies measure brain activity in light repetitive task using EEG [5] to measure drowsiness or fatigue on drivers [6] [7] and night work [8] [9]. 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 [10]. Another problem is imposed by the inter-variability and intra-variability in EEG dynamics accompanying loss of alertness [11].

Source: Fatigue detection and estimation using auto-regression analysis in EEG – IEEE Xplore Document

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[Abstract] Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback

Abstract:

The objective of this work was to design and experiment a robotic hand rehabilitation device integrated with a wireless EEG system, going towards patient active participation maximization during the exercise. This has been done through i) hand movement actively triggered by patients muscular activity as revealed by electromyographic signals (i.e., a target hand movement for the rehabilitation session is defined, the patient is required to start the movement and only when the muscular activity overcomes a predefined threshold, the patient-initiated movement is supported); ii) an EEG-based biofeedback implemented to make the user aware of his/her level of engagement (i.e., brain rhythms power ratio Beta/Alpha). The designed system is composed by the Gloreha hand rehabilitation glove, a device for electromyographic signals recording, and a wireless EEG headset. A strong multidisciplinary approach was the base to reach this goal, which is the fruitful background of the Think and Go project. Within this project, research institutes (Politecnico di Milano), clinical centers (INRCA-IRCCS), and companies (ab medica s.p.a., Idrogent, SXT) have worked together throughout the development of the integrated robotic hand rehabilitation device. The integrated device has been tested on a small pilot group of healthy volunteers. All the users were able to calibrate and correctly use the system, and they reported that the system was more challenging to be used with respect to the standard passive hand mobilization session, and required more attention and involvement. The results obtained during the preliminary tests are encouraging, and demonstrate the feasibility of the proposed approach.

Source: Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback – IEEE Xplore Document

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[VIDEO] Where to place electrodes – dorsiflexion with eversion – YouTube

Δημοσιεύτηκε στις 7 Νοε 2016

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
For more information, see our website:
http://www.odstockmedical.com/knowled…

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