Posts Tagged EEG

[WEB SITE] BrainTrain

 

BRAINTRAIN will improve and adapt the methods of real-time fMRI neurofeedback (fMRI-NF) for clinical use, including the combination with electroencephalography (EEG) and the development of standardised procedures for the mapping of brain networks that can be targeted with neurofeedback.

Its core component will be the exploration of the efficacy of fMRI-NF in selected mental and neurodevelopmental disorders that involve motivational, emotional and social neural systems. The ultimate goals of BRAINTRAIN are therefore to :

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  • Develop new or optimize existing imaging technologies,
  • Validate their application as a therapeutic tool to mental and behavioural disorders by integrating imaging data with complementarity knowledge resulting bioinformatics and clinical data,
  • Allow the diagnosis of mental disorders at the pre-symptomatic stage or early during development,
  • Better measure disease progression.
  • Develop transfer technologies for fMRI-NF through EEG and serious games.

BRAINTRAIN is innovative in the development of new real-time imaging technologies e.g. new sequences, image reconstruction methods and data analysis software. This will also be the first clinical testing of fMRI-NF in a set of disorders with extraordinary socioeconomic and public health impact.

The project started in November 2013 and will last four years. It is coordinated by Cardiff University (Professor David Linden, Wales, UK).

BRAINTRAIN is a European research network (Collaborative Project) supported by the European Commission under the Health Cooperation Work Programme of the 7th Framework Programme, under the Grant Agreement n°602186.

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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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[ARTICLE] Transcranial Direct Current Stimulation Modulates Neuronal Activity and Learning in Pilot Training – Full Text HTML

Skill acquisition requires distributed learning both within (online) and across (offline) days to consolidate experiences into newly learned abilities. In particular, piloting an aircraft requires skills developed from extensive training and practice. Here, we tested the hypothesis that transcranial direct current stimulation (tDCS) can modulate neuronal function to improve skill learning and performance during flight simulator training of aircraft landing procedures.

Thirty-two right-handed participants consented to participate in four consecutive daily sessions of flight simulation training and received sham or anodal high-definition-tDCS to the right dorsolateral prefrontal cortex (DLPFC) or left motor cortex (M1) in a randomized, double-blind experiment. Continuous electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were collected during flight simulation, n-back working memory, and resting-state assessments. tDCS of the right DLPFC increased midline-frontal theta-band activity in flight and n-back working memory training, confirming tDCS-related modulation of brain processes involved in executive function. This modulation corresponded to a significantly different online and offline learning rates for working memory accuracy and decreased inter-subject behavioral variability in flight and n-back tasks in the DLPFC stimulation group. Additionally, tDCS of left M1 increased parietal alpha power during flight tasks and tDCS to the right DLPFC increased midline frontal theta-band power during n-back and flight tasks.

These results demonstrate a modulation of group variance in skill acquisition through an increasing in learned skill consistency in cognitive and real-world tasks with tDCS. Further, tDCS performance improvements corresponded to changes in electrophysiological and blood-oxygenation activity of the DLPFC and motor cortices, providing a stronger link between modulated neuronal function and behavior.

Continue —> Frontiers | Transcranial Direct Current Stimulation Modulates Neuronal Activity and Learning in Pilot Training | Frontiers in Human Neuroscience

Figure 1. Experimental design. (A) Experiment timeline depicting the relative timing of each task (see Table 1 for descriptions of each task). The N-Back and Easy Landing tasks are highlighted, and the duration of tDCS is depicted in red. (B) An example of 6 trials of the N-Back task is shown. 1-back orientation and location match trials are highlighted in yellow. (C) The flight simulator, neuroimaging (EEG and FNIRS) and tDCS setup is shown with on a subject (1). Flight simulator equipment includes three-panel display, a radio panel (2), an instrument panel (3) with (from left to right) compass, altimeter, airspeed indicator, vertical speed indicator, and turn/slip indicator, a multi-panel (4) with (from left to right) autopilot settings, auto throttle switch, flaps switch, and elevator trim wheel, yoke (5), and throttle quadrant system (6). (D) Autopilot flight path for the Easy Landing task is shown in 3 dimensions, color-coded by vertical speed. Screenshots for initial descent, approach, and landing are also shown.

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[Abstract] Detecting voluntary gait intention of chronic stroke patients towards top-down gait rehabilitation using EEG

Abstract:

One of the recent trends in gait rehabilitation is to incorporate bio-signals, such as electromyography (EMG) or electroencephalography (EEG), for facilitating neuroplasticity, i.e. top-down approach. In this study, we investigated decoding stroke patients’ gait intention through a wireless EEG system. To overcome patient-specific EEG patterns due to impaired cerebral cortices, common spatial patterns (CSP) was employed. We demonstrated that CSP filter can be used to maximize the EEG signal variance-ratio of gait and standing conditions. Finally, linear discriminant analysis (LDA) classification was conducted, whereby the average accuracy of 73.2% and the average delay of 0.13 s were achieved for 3 chronic stroke patients. Additionally, we also found out that the inverse CSP matrix topography of stroke patients’ EEG showed good agreement with the patients’ paretic side.

Source: IEEE Xplore Document – Detecting voluntary gait intention of chronic stroke patients towards top-down gait rehabilitation using EEG

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[ARTICLE] Computational Pipeline for NIRS-EEG Joint Imaging of tDCS-Evoked Cerebral Responses – An Application in Ischemic Stroke – Full Text

Transcranial direct current stimulation (tDCS) modulates cortical neural activity and hemodynamics. Electrophysiological methods (electroencephalography-EEG) measure neural activity while optical methods (near-infrared spectroscopy-NIRS) measure hemodynamics coupled through neurovascular coupling (NVC). Assessment of NVC requires development of NIRS-EEG joint-imaging sensor montages that are sensitive to the tDCS affected brain areas. In this methods paper, we present a software pipeline incorporating freely available software tools that can be used to target vascular territories with tDCS and develop a NIRS-EEG probe for joint imaging of tDCS-evoked responses. We apply this software pipeline to target primarily the outer convexity of the brain territory (superficial divisions) of the middle cerebral artery (MCA). We then present a computational method based on Empirical Mode Decomposition of NIRS and EEG time series into a set of intrinsic mode functions (IMFs), and then perform a cross-correlation analysis on those IMFs from NIRS and EEG signals to model NVC at the lesional and contralesional hemispheres of an ischemic stroke patient. For the contralesional hemisphere, a strong positive correlation between IMFs of regional cerebral haemoglobin oxygen saturation and the log-transformed mean-power time-series of IMFs for EEG with a lag of about -15sec was found after a cumulative 550 sec stimulation of anodal tDCS. It is postulated that system identification, for example using a continuous-time autoregressive model, of this coupling relation under tDCS perturbation may provide spatiotemporal discriminatory features for the identification of ischemia. Furthermore, portable NIRS-EEG joint imaging can be incorporated into brain computer interfaces to monitor tDCS-facilitated neurointervention as well as cortical reorganization.

Continue —> Frontiers | Computational Pipeline for NIRS-EEG Joint Imaging of tDCS-Evoked Cerebral Responses—An Application in Ischemic Stroke | Neural Technology

Figure 1. Current density magnitude (normJ) at the scalp surface (A), skull surface (B), CSF surface (C), gray matter surface (D), and white matter surface (E) with 1 mA F3 anodal and Cz cathodal tDCS.

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[ARTICLE] Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis – Full Text

Abstract

Background

The use of Brain–Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain’s capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training.

Methods

In order to investigate how different BCI paradigms impact brain activation, we designed 3 experimental conditions in a within-subject design, including an immersive Multimodal Virtual Reality with Motor Priming (VRMP) condition where users had to perform motor-execution before BCI training, an immersive Multimodal VR condition, and a control condition with standard 2D feedback. Further, these were also compared to overt motor-execution. Finally, a set of questionnaires were used to gather subjective data on Workload, Kinesthetic Imagery and Presence.

Results

Our findings show increased capacity to modulate and enhance brain activity patterns in all extracted EEG rhythms matching more closely those present during motor-execution and also a strong relationship between electrophysiological data and subjective experience.

Conclusions

Our data suggest that both VR and particularly MP can enhance the activation of brain patterns present during overt motor-execution. Further, we show changes in the interhemispheric EEG balance, which might play an important role in the promotion of neural activation and neuroplastic changes in stroke patients in a motor-imagery neurofeedback paradigm. In addition, electrophysiological correlates of psychophysiological responses provide us with valuable information about the motor and affective state of the user that has the potential to be used to predict MI-BCI training outcome based on user’s profile. Finally, we propose a BCI paradigm in VR, which gives the possibility of motor priming for patients with low level of motor control.

Continue —> Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 2 MI-BCI training conditions. (a) VRMP: the user has to perform motor priming by mapping his/her hand movements into the virtual environment. (b) VR: the user has to perform training through simultaneous motor action observation and MI, before moving to the MI task were he/she has to control the virtual hands through MI. (c) Control: MI training with standard feedback through arrows-and-bars

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[Abstract] A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke.

ABSTRACT

Stroke is a leading cause of acquired disability resulting in distal upper extremity functional motor impairment. Stroke mortality rates continue to decline with advances in healthcare and medical technology. This has led to an increased demand for advanced, personalized rehabilitation.
Survivors often experience some level of spontaneous recovery shortly after their stroke event, yet reach a functional plateau after which there is exiguous motor recovery. Nevertheless, studies have demonstrated the potential for recovery beyond this plateau.
Non-traditional neurorehabilitation techniques, such as those incorporating the brain-computer interface (BCI), are being investigated for rehabilitation. BCIs may offer a gateway to the brain’s plasticity and revolutionize how humans interact with the world.
Non-invasive BCIs work by closing the proprioceptive feedback loop with real-time, multi-sensory feedback allowing for volitional modulation of brain signals to assist hand function. BCI technology potentially promotes neuroplasticity and Hebbian-based motor recovery by rewarding cortical activity associated with sensory-motor rhythms through use with a variety of self-guided and assistive modalities.

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Source: A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke – Expert Review of Medical Devices – Volume 13, Issue 5

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[Abstract] A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke.

ABSTRACT

Stroke is a leading cause of acquired disability resulting in distal upper extremity functional motor impairment. Stroke mortality rates continue to decline with advances in healthcare and medical technology. This has led to an increased demand for advanced, personalized rehabilitation.
Survivors often experience some level of spontaneous recovery shortly after their stroke event, yet reach a functional plateau after which there is exiguous motor recovery. Nevertheless, studies have demonstrated the potential for recovery beyond this plateau.
Non-traditional neurorehabilitation techniques, such as those incorporating the brain-computer interface (BCI), are being investigated for rehabilitation. BCIs may offer a gateway to the brain’s plasticity and revolutionize how humans interact with the world. Non-invasive BCIs work by closing the proprioceptive feedback loop with real-time, multi-sensory feedback allowing for volitional modulation of brain signals to assist hand function. BCI technology potentially promotes neuroplasticity and Hebbian-based motor recovery by rewarding cortical activity associated with sensory-motor rhythms through use with a variety of self-guided and assistive modalities.

View all related articles

Source: A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke – Expert Review of Medical Devices – Volume 13, Issue 5

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[ARTICLE] On Usage of EEG Brain Control for Rehabilitation of Stroke Patients – Full Text PDF

ABSTRACT

This paper demonstrates rapid prototyping of a stroke rehabilitation system consisting of an interactive 3D virtual reality computer game environment interfaced with an EEG headset for control and interaction using brain waves. The system is intended for training and rehabilitation of partially monoplegic stroke patients and uses lowcost commercial-off-the-shelf products like the Emotiv EPOC EEG headset and the Unity 3D game engine.

A number of rehabilitation methods exist that can improve motor control and function of the paretic upper limb in stroke survivors. Unfortunately, most of these methods are commonly characterised by a number of drawbacks that can limit intensive treatment, including being repetitive, uninspiring, and labour intensive; requiring one-on-one manual interaction and assistance from a therapist, often for several weeks; and involve equipment and systems that are complex and expensive and cannot be used at home but only in hospitals and institutions by trained personnel.

Inspired by the principles of mirror therapy and game-stimulated rehabilitation, we have developed a first prototype of a game-like computer application that tries to avoid these drawbacks. For rehabilitation purposes, we deprive the patient of the view of the paretic hand while being challenged with controlling a virtual hand in a simulated 3D game environment only by means of EEG brain waves interfaced with the computer.

Whilst our system is only a first prototype, we hypothesise that by iteratively improving its design through refinements and tuning based on input from domain experts and testing on real patients, the system can be tailored for being used together with a conventional rehabilitation programme to improve patients’ ability to move the paretic limb much in the same vain as mirror therapy.

Our proposed system has several advantages, including being game-based, customisable, adaptive, and extendable. In addition, when compared with conventional rehabilitation methods, our system is extremely low-cost and flexible, in particular because patients can use it in the comfort of their homes, with little or no need for professional human assistance. Preliminary tests are carried out to highlight the potential of the proposed rehabilitation system, however, in order to measure its efficiency in rehabilitation, the system must first be improved and then run through an extensive field test with a sufficiently large group of patients and compared with a control group.

Continue —> Full Text PDF

 

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[Abstract] Interfacing brain with computer to improve communication and rehabilitation after brain damage.

Abstract

Communication and control of the external environment can be provided via brain–computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke).

BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment).

Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other.

For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm.

 

Source: Interfacing brain with computer to improve communication and rehabilitation after brain damage

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