Posts Tagged EEG

[ARTICLE] Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report – Full Text

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.

Introduction

Worldwide, stroke is a leading cause of adult long-term disability (Mozaffarian et al., 2015). From those who survive, an increased number is suffering with severe cognitive and motor impairments, resulting in loss of independence in their daily life such as self-care tasks and participation in social activities (Miller et al., 2010). Rehabilitation following stroke is a multidisciplinary approach to disability which focuses on recovery of independence. There is increasing evidence that chronic stoke patients maintain brain plasticity, meaning that there is still potential for additional recovery (Page et al., 2004). Traditional motor rehabilitation is applied through physical therapy and/or occupational therapy. Current approaches of motor rehabilitation include functional training, strengthening exercises, and range of movement exercises. In addition, techniques based on postural control, stages of motor learning, and movement patterns have been proposed such as in the Bobath concept and Bunnstrom approach (amongst others) (Bobath, 1990). After patients complete subacute rehabilitation programs, many still show significant upper limb motor impairment. This has important functional implications that ultimately reduce their quality of life. Therefore, alternative methods to maximize brain plasticity after stroke need to be developed.

So far, there is growing evidence that action observation (AO) (Celnik et al., 2008) and motor imagery (MI) improve motor function (Mizuguchi and Kanosue, 2017) but techniques based on this paradigm are not widespread in clinical settings. As motor recovery is a learning process, the potential of MI as a training paradigm relies on the availability of an efficient feedback system. To date, a number of studies have demonstrated the positive impact of virtual-reality (VR) based on neuroscientific grounds on recovery, with proven effectiveness in the stroke population (Bermúdez i Badia et al., 2016). However, patients with no active movement cannot benefit from current VR tools due to low range of motion, pain, fatigue, etc. (Trompetto et al., 2014). Consequently, the idea of directly training the central nervous system was promoted by establishing an alternative pathway between the user’s brain and a computer system.

This is possible by using electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), since they can provide an alternative non-muscular channel for communication and control to the external world (Wolpaw et al., 2002), while they could also provide a cost-effective solution for training (Vourvopoulos and Bermúdez, 2016b). In rehabilitation, BCIs could offer a unique tool for rehabilitation since they can stimulate neural networks through the activation of mirror neurons (Rizzolatti and Craighero, 2004) by means of action-observation (Kim et al., 2016), motor-intent and motor-imagery (Neuper et al., 2009), that could potentially lead to post-stroke motor recovery. Thus, BCIs could provide a backdoor to the activation of motor neural circuits that are not stimulated through traditional rehabilitation techniques.

In EEG-based BCI systems for motor rehabilitation, Alpha (8–12 Hz) and Beta (12–30 Hz) EEG rhythms are utilized since they are related to motor planning and execution (McFarland et al., 2000). During a motor attempt or motor imagery, the temporal pattern of the Alpha rhythms desynchronizes. This rhythm is also named Rolandic Mu-rhythm or the sensorimotor rhythm (SMR) because of its localization over the sensorimotor cortices. Mu-rhythms are considered indirect indications of functioning of the mirror neuron system and general sensorimotor activity (Kropotov, 2016). These are often detected together with Beta rhythm changes in the form of an event-related desynchronization (ERD) when a motor action is executed (Pfurtscheller and Lopes da Silva, 1999). These EEG patterns are primarily detected during task-based EEG (e.g., when the participant is actively moving or imagining movement) and they are of high importance in MI-BCIs for motor rehabilitation.

A meta-analysis of nine studies (combined N = 235, sample size variation 14 to 47) evaluated the clinical effectiveness of BCI-based rehabilitation of patients with post-stroke hemiparesis/hemiplegia and concluded that BCI technology could be effective compared to conventional treatment (Cervera et al., 2018). This included ischemic and hemorrhagic stroke in both subacute and chronic stages of stoke, between 2 to 8 weeks. Moreover, there is evidence that BCI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis (Ramos-Murguialday et al., 2019), while overall BCI’s are starting to prove their efficacy as rehabilitative technologies in patients with severe motor impairments (Chaudhary et al., 2016).

The feedback modalities used for BCI motor rehabilitation include: non-embodied simple two-dimensional tariffs on a screen (Prasad et al., 2010Mihara et al., 2013), embodied avatar representation of the patient on a screen or with augmented reality (Holper et al., 2010Pichiorri et al., 2015), neuromuscular electrical stimulation (NMES) (Kim et al., 2016Biasiucci et al., 2018). and robotic exoskeletal orthotic movement facilitation (Ramos-Murguialday et al., 2013Várkuti et al., 2013Ang et al., 2015). In addition, it has been shown that multimodal feedback lead to a significantly better performance in motor-imagery (Sollfrank et al., 2016) but also multimodal feedback combined with motor-priming, (Vourvopoulos and Bermúdez, 2016a). However, there is no evidence which modalities are more efficient in stroke rehabilitation are.

Taking into account all previous findings in the effects of multimodal feedback in MI training, the purpose of this case study is to examine the effect of the MI paradigm as a treatment for post-stroke upper limb motor dysfunction using the NeuRow BCI-VR system. This is achieved through the acquisition of clinical scales, dynamics of EEG during the BCI treatment, and brain activation as measured by functional MRI (fMRI). NeuRow is an immersive VR environment for MI-BCI training that uses an embodied avatar representation of the patient arms and haptic feedback. The combination of MI-BCIs with VR can reinforce activation of motor brain areas, by promoting the illusion of physical movement and the sense of embodiment in VR (Slater, 2017), and hence further engaging specific neural networks and mobilizing the desired neuroplastic changes. Virtual representation of body parts paves the way to include action observation during treatment. Moreover, haptic feedback is added since a combination of feedback modalities could prove to be more effective in terms of motor-learning (Sigrist et al., 2013). Therefore, the target of this system is to be used by patients with low or no levels of motor control. With this integrated BCI-VR approach, severe cases of stroke survivors may be admitted to a VR rehabilitation program, complementing traditional treatment.

Methodology

Patient Profile

In this pilot study we recruited a 60 years old male patient with left hemiparesis following cerebral infarct in the right temporoparietal region 10 months before. The participant had corrected vision through eyewear, he had 4 years of schooling and his experience with computers was reported as low. Moreover, the patient was on a low dose of diazepam (5 mg at night to help sleep), dual antiplatelet therapy, anti-hypertensive drug and metformin. Hemiparesis was associated with reduced dexterity and fine motor function; however, sensitivity was not affected. Other sequelae of the stroke included hemiparetic gait and dysarthria. Moreover, a mild cognitive impairment was identified which did not interfere with his ability to perform the BCI-VR training. The patient had no other relevant comorbidities. Finally, the patient was undergoing physiotherapy and occupational therapy at the time of recruitment and had been treated with botulinum toxin infiltration 2 months before due to focal spasticity of the biceps brachii.

Intervention Protocol

The patient underwent a 3-weeks intervention with NeuRow, resulting in 10 BCI sessions of a 15 min of exposure in VR training per session. Clinical scales, motor imagery capability assessment, and functional -together with structural- MRI data had been gathered in three time-periods: (1) before (serving as baseline), (2) shortly after the intervention and (3) one-month after the intervention (to assess the presence of long-term changes). Finally, electroencephalographic (EEG) data had been gathered during all sessions, resulting in more than 20 datasets of brain electrical activity.

The experimental protocol was designed in collaboration with the local healthcare system of Madeira, Portugal (SESARAM) and approved by the scientific and ethic committees of the Central Hospital of Funchal. Finally, written informed consent was obtained from the participant upon recruitment for participating to the study but also for the publication of the case report in accordance with the 1964 Declaration of Helsinki.

Assessment Tools

A set of clinical scales were acquired including the following:

1. Montreal Cognitive Assessment (MoCA). MoCA is a cognitive screening tool, with a score range between 0 and 30 (a score greater than 26 is considered to be normal) validated also for the Portuguese population, (Nasreddine et al., 2005).

2. Modified Ashworth scale (MAS). MAS is a 6-point rating scale for measuring spasticity. The score range is 0, 1, 1+, 2, 3, and 4 (Ansari et al., 2008).

3. Fugl-Meyer Assessment (FMA). FMA is a stroke specific scale that assesses motor function, sensation, balance, joint range of motion and joint pain. The motor domain for the upper limb has a maximum score of 66 (Fugl-Meyer et al., 1975).

4. Stroke Impact Scale (SIS). SIS is a subjective scale of the perceived stroke impact and recovery as reported by the patient, validated for the Portuguese population. The score of each domain of the questionnaire ranges from 0 to 100 (Duncan et al., 1999).

5. Vividness of Movement Imagery Questionnaire (VMIQ2). VMIQ2 is an instrument that assess the capability of the participant to perform imagined movements from external perspective (EVI), internal perspective imagined movements (IVI) and finally, kinesthetic imagery (KI) (Roberts et al., 2008).

NeuRow BCI-VR System

EEG Acquisition

For EEG data acquisition, the Enobio 8 (Neuroelectrics, Barcelona, Spain) system was used. Enobio is a wearable wireless EEG sensor with 8 EEG channels for the recording and visualization of 24-bit EEG data at 500 Hz and a triaxial accelerometer. The spatial distribution of the electrodes followed the 10–20 system configuration (Klem et al., 1999) with the following electrodes over the somatosensory and motor areas: Frontal-Central (FC5, FC6), Central (C1, C2, C3, C4), and Central-Parietal (CP5, CP6) (Figure 1A). The EEG system was connected via Bluetooth to a dedicated desktop computer, responsible for the EEG signal processing and classification, streaming the data via UDP through the Reh@Panel (RehabNet Control Panel) for controlling the virtual environment. The Reh@Panel is a free tool that acts as a middleware between multiple interfaces and virtual environments (Vourvopoulos et al., 2013).

FIGURE 1

Figure 1. Experimental setup, including: (A) the wireless EEG system; (B) the Oculus HMD, together with headphones reproducing the ambient sound from the virtual environment; (C) the vibrotactile modules supported by a custom-made table-tray, similar to the wheelchair trays used for support; (D) the visual feedback with NeuRow game. A written informed consent was obtained for the publication of this image.

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[Abstract] The Role of EEG in the Erroneous Diagnosis of Epilepsy

Summary

Errors in diagnosis are relatively common in medicine and occur in all specialties. The consequences can be serious for both patients and physicians. Errors in neurology are often because of the overemphasis on “tests” over the clinical picture. The diagnosis of epilepsy in general is a clinical one and is typically based on history. Epilepsy is more commonly overdiagnosed than underdiagnosed. An erroneous diagnosis of epilepsy is often the result of weak history and an “abnormal” EEG. Twenty-five to 30% of patients previously diagnosed with epilepsy who did not respond to initial antiepileptic drug treatment do not have epilepsy. Most patients misdiagnosed with epilepsy turn out to have either psychogenic nonepileptic attacks or syncope. Reasons for reading a normal EEG as an abnormal one include over-reading normal variants or simple fluctuations of background rhythms. Reversing the diagnosis of epilepsy is challenging and requires reviewing the “abnormal” EEG, which can be difficult. The lack of mandatory training in neurology residency programs is one of the main reasons for normal EEGs being over-read as abnormal. Tests (including EEG) should not be overemphasized over clinical judgment. The diagnosis of epilepsy can be challenging, and some seizure types may be underdiagnosed. Frontal lobe hypermotor seizures may be misdiagnosed as psychogenic events. Focal unaware cognitive seizures in elderly maybe be blamed on dementia, and ictal or interictal psychosis in frontal and temporal lobe epilepsies may be mistaken for a primary psychiatric disorder.

via The Role of EEG in the Erroneous Diagnosis of Epilepsy : Journal of Clinical Neurophysiology

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[ARTICLE] An Attention-Controlled Hand Exoskeleton for the Rehabilitation of Finger Extension and Flexion Using a Rigid-Soft Combined Mechanism – Full Text

Hand rehabilitation exoskeletons are in need of improving key features such as simplicity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control. This article presents a brain-controlled hand exoskeleton based on a multi-segment mechanism driven by a steel spring. Active rehabilitation training is realized using a threshold of the attention value measured by an electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. We present a prototype implementation of this rigid-soft combined multi-segment mechanism with active training and provide a preliminary evaluation. The experimental results showed that the proposed mechanism could generate enough range of motion with a single input by distributing an actuated linear motion into the rotational motions of finger joints during finger flexion/extension. The average attention value in the experiment of concentration with visual guidance was significantly higher than that in the experiment without visual guidance. The feasibility of the attention-based control with visual guidance was proven with an overall exoskeleton actuation success rate of 95.54% (14 human subjects). In the exoskeleton actuation experiment using the general threshold, it performed just as good as using the customized thresholds; therefore, a general threshold of the attention value can be set for a certain group of users in hand exoskeleton activation.

Introduction

Hand function is essential for our daily life (Heo et al., 2012). In fact, only partial loss of the ability to move our fingers can inhibit activities of daily living (ADL), and even reduce our quality of life (Takahashi et al., 2008). Research on robotic training of the wrist and hand has shown that improvements in finger or wrist level function can be generalized across the arm (Lambercy et al., 2011). Finger muscle weakness is believed to be the main cause of loss of hand function after strokes, especially for finger extension (Cruz et al., 2005Kamper et al., 2006). Hand rehabilitation requires repetitive task exercises, where a task is divided into several movements and patients are asked to practice those movements to improve their hand strength, range of motion, and motion accuracy (Takahashi et al., 2008Ueki et al., 2012). High costs of traditional treatments often prevent patients from spending enough time on the necessary rehabilitation (Maciejasz et al., 2014). In recent years, robotic technologies have been applied in motion rehabilitation to provide training assistance and quantitative assessments of recovery. Studies show that intense repetitive movements with robotic assistance can significantly improve the hand motor functions of patients (Takahashi et al., 2008Ueki et al., 2008Kutner et al., 2010Carmeli et al., 2011Wolf et al., 2006).

Patients should be actively involved in training to achieve better rehabilitation results (Teo and Chew, 2014Li et al., 2018). Motor rehabilitation has implemented Brain Computer Interface (BCI) methods as one of the means to detect human movement intent and get patients to be actively involved in the motor training process (Teo and Chew, 2014Li et al., 2018). Motor imagery-based BCIs (Jiang et al., 2015Pichiorri et al., 2015Kraus et al., 2016Vourvopoulos and Bermúdez I Badia, 2016), movement-related cortical potentials-based BCIs (Xu et al., 2014Bhagat et al., 2016), and steady-state motion visual evoked potential-based BCIs (Zhang et al., 2015) have been used to control rehabilitation robots. However, the high cost and complexity of the preparation in utilizing these methods mean that most current BCI devices are more suitable for research purposes than clinical practices. This is attributable to the fact that the ease of use and device cost are two main factors to consider during the selection of human movement intent detection based on BCIs for practical use (van Dokkum et al., 2015Li et al., 2018). Therefore, non-invasive, easy-to-install BCIs that are convenient to use with acceptable accuracy should be introduced to hand rehabilitation robot control.

Owing to the versatility and complexity of human hands, developing hand exoskeleton robots for rehabilitation assistance in hand movements is challenging (Heo et al., 2012Arata et al., 2013). In recent years, hand exoskeleton devices have drawn much research attention, and the results of current research look promising (Heo et al., 2012). Hand exoskeleton devices mainly use linkage, wire, or hydraulically/pneumatically driven mechanisms (Polygerinos et al., 2015a). The rigid mechanical design of linkage-based mechanisms provides robustness and reliability of power transmission, and has been widely applied in hand exoskeletons (Tong et al., 2010Ito et al., 2011Arata et al., 2013Cui et al., 2015Polygerinos et al., 2015a). However, the safety problem of misalignment between the human finger joints and the exoskeleton joints may occur during rehabilitation movements (Heo et al., 2012Cui et al., 2015). Compensation approaches used in current studies make the mechanism more complicated (Nakagawara et al., 2005Fang et al., 2009Ho et al., 2011). Pneumatic and hydraulic soft hand exoskeletons, which are made of flexible materials, are proposed to assist hand opening or closing (Ang and Yeow, 2017Polygerinos et al., 2015aYap et al., 2015b). However, despite bi-directional assistance—namely finger flexion and extension—being essential for hand rehabilitation (Iqbal et al., 2014), a large group of current soft hand exoskeleton devices only provide finger flexion assistance (Connelly et al., 2010Polygerinos et al., 20132015aYap et al., 2015ab). Wire-driven mechanisms can also be complex to transmit bi-directional movements since wires can only transmit forces along one direction (In et al., 2015Borboni et al., 2016). In order to transmit bi-directional movements, a tendon-driven hand exoskeleton was proposed, where the tendon works as a tendon during the extension movement and as compressed flexible beam constrained into rectilinear slides mounted on the distal sections of the glove during flexion (Borboni et al., 2016). Arata et al. (2013) attempted to avoid wire extension and other associated issues by proposing a hand exoskeleton with a three-layered sliding spring mechanism. Hand rehabilitation exoskeleton devices are still seeking to achieve key features such as low complexity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control.

In this article, we describe the design and characterization of a novel multi-segment mechanism driven by one layer of a steel spring that can assist both extension and flexion of the finger. Thanks to the inherent features of this multi-segment mechanism, joint misalignment between the device and the human finger is no longer a problem, enhancing the simplicity and flexibility of the device. Moreover, its compliance makes the hand exoskeleton safe for human-robotic interaction. This mechanism can generate enough range of motion with a single input by distributing an actuated linear motion to the rotational motions of finger joints. Active rehabilitation training is realized by using a threshold of the attention value measured by a commercialized electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. Features of this hand exoskeleton include active involvement of patients, low complexity, compactness, bi-directional actuation, low cost, portability, and safe human-robotic interaction. The main contributions of this article include: (1) prototyping and evaluation of a hand exoskeleton with a rigid-soft combined multi-segment mechanism driven by one layer of a steel spring with a sufficient output force capacity; (2) using attention-based BCI control to increase patients’ participation in exoskeleton-assisted hand rehabilitation; and (3) determining the threshold of attention value for our attention-based hand rehabilitation robot control.

Exoskeleton Design

Design Requirements

The target users are stroke survivors during flaccid paralysis period who need continuous passive motion training of their hands. They should also be able to focus their attention on motion rehabilitation training for at least a short period of time. For the purpose of hand rehabilitation, an exoskeleton should have minimal ADL interference and have the ability to generate adequate forces to perform hand flexion and extension with a range of motion that is similar or slightly lower than the motion range of a natural finger.

To achieve minimal ADL interference, the device is to be confined to the back of the finger and the width of the device should not exceed the finger width. Here, the width and height constraints of the exoskeleton on the back of the finger are both 20 mm. Low weight of the rehabilitation systems is a key requirement to allow practical use by a wide stroke population (Nycz et al., 2016). Therefore, the target weight of the exoskeleton should be as light as possible to make the patient feel more comfortable to wear it. The typical weight of other hand exoskeletons is in the range of 0.7 kg–5 kg (CyberGlove Systems Inc., 2016Delph et al., 2013Polygerinos et al., 2015aRehab-Robotics Company Ltd., 2019). In this article, the target weight of the exoskeleton is less than 0.5 kg.

There are 15 joints in the human hand. The thumb joint consists of an interphalangeal joint (IPJ), a metacarpophalangeal joint (MPJ), and a carpometacarpal joint (CMJ). Each of the other four fingers has three joints including a metacarpophalangeal joint (MCPJ), a proximal interphalangeal joint (PIPJ), and a distal interphalangeal joint (DIPJ). The hand exoskeleton must have three bending degrees of freedom (DOF) to exercise the three joints of the finger. For some rehabilitation applications, it is unnecessary for each of the MCPJ, PIPJ, and DIPJ of the human finger to have independent motion as long as the whole range of motion of the finger is covered. Tripod grasping requires the MPJ and IPJ of the thumb to bend around 51° and 27°; MCPJ, PIPJ, and DIPJ of the index finger to bend around 46°, 48°, and 12°; and for the middle finger to bend around 46°, 54°, and 12° (In et al., 2015). For the execution speed of rehabilitation exercises, physiotherapists suggest a lower speed than 20 s for a flexion/extension cycle of a finger joint (Borboni et al., 2016). It has to be stressed that hyperextension of all these joints should always be carefully avoided.

The exerted force to the finger should be able to enable continuous passive motion training. In addition, the output force should help the patient to generate grasping forces required to manipulate objects in ADL. Pinch forces required to complete functional tasks are typically below 20 N (Smaby et al., 2004). Polygerinos et al. (2015b) estimated each robot finger should exert a distal tip force of about 7.3 N to achieve a palmar grasp—namely four fingers against the palm of the hand—to pick up objects less than 1.5 kg. Existing devices can provide a maximum transmission output force between 7 N and 35 N (Kokubun et al., 2013In et al., 2015Polygerinos et al., 2015bBorboni et al., 2016Nycz et al., 2016).

The design should allow some customization to hand size and adaptability to different patient statuses and different stages of rehabilitation.

Rigid-Soft Combined Mechanism

Based on our established design requirements, a hand exoskeleton was designed and constructed (see Figure 1). In our design, each finger was driven by one actuator for finger extension and flexion, resulting in a highly compact device. A multi-segment mechanism with a spring layer was proposed. It has respectable adaptability, thus avoiding joint misalignment problems. A three-dimensional model of a single finger actuator is shown in Figure 1A. This finger actuator contained a linear motor, a steel strap, and a multi-segment mechanism. As shown in Figure 1B, the spring layer bended and slid because of the linear motion input provided by the linear actuator. The structure then became like a circular sector. When the structure was attached to a finger, it supported the finger flexion/extension motion. Five finger actuators were attached to a fabric glove via Velcro straps and five linear motors were attached to a rigid part which was fixed to the forearm by a Velcro strap. Each steel strap was attached to a motor by a small rigid 3D-printed part. It should be noted that the current structure is not applicable to thumb adduction/abduction.

Figure 1. Design of the hand exoskeleton: (A) CAD drawing of the index finger acuator; (B) bending motion generated by the proposed mutli-segment mechanism with a spring layer; (C) segment thicknesses (unit: mm); and (D) overview of the hand exoskeleton prototype.

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[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.

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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.

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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.

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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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[Editorial] Functional brain mapping of epilepsy networks: methods and applications – Neuroscience

This multidisciplinary research topic is a collection of contemporary advances in neuroimaging applied to mapping functional brain networks in epilepsy. With technology such as simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) now more readily available, it is possible to non-invasively map epileptiform activity throughout the entire brain at millimetre resolution. This research topic includes original research studies, technical notes and reviews of the field. Due to the multidisciplinary nature of the domain, the topic spans two journals: Frontiers in Neurology (Section: Epilepsy) and Frontiers in Neuroscience (Section: Brain Imaging Methods).
In this editorial we consider the outcomes of the multidisciplinary work presented in the topic. With the benefit of time elapsed since the original papers were published, we can see that the works are making a substantial impact in the field. At the time of writing, this topic had well over 27,000 full-paper downloads (including over 18,000 for the 15 papers in the Epilepsy section, and over 9,000 for the 8 papers in the Brain Imaging Methods section). Several papers in the topic have climbed the tier in Frontiers and received an associated invited commentary, demonstrating there is substantial interest in this research area.
Reviews
The topic’s review papers set the scene for the original research papers and synthesise contemporary thinking in epilepsy research and neuroimaging methods. We see that Epilepsy, whether of a “generalised” or “focal” origin, is increasingly recognised as a disorder of large-scale brain networks. At one level it is self-evident that otherwise healthy functional networks are recruited during epileptic activity, as this is what generates patient perceptions of their epileptic aura. For example, the epileptic aura of mesial temporal lobe epilepsy can include an intense sensation of familiarity (déjà vu) associated with involvement of the hippocampus, and unpleasant olfactory auras which may reflect involvement of adjacent olfactory cortex. As seizures spread more widely throughout the brain, presumably along pre-existing neural pathways, patients lose control of certain functions; for example, their motor system in the case of generalised convulsions, or aspects of awareness in seizures that remain localised to non-motor brain regions. Yet these functions return when the seizure abates, implying involved brain regions are also responsible for normal brain function. What has been less clear, and difficult to investigate until the advent of functional neuroimaging, is precisely which brain networks are involved (especially in ‘generalised’ epilepsy syndromes), and the extent to which functional networks are perturbed during seizures, inter-ictal activity, and at other times.
Functional imaging evidence of brain abnormalities in temporal lobe epilepsy is explored in (Caciagli et al., 2014), including evidence of dysfunction in limbic and other specific brain networks, as well as global changes in network topography derived from resting-state fMRI. Archer et al systematically review the functional neuroimaging of a particularly severe epilepsy phenotype, Lennox-Gastaut Syndrome (LGS), illustrating well how different forms of brain pathology can manifest in a similar clinical phenotype, simply by the nature of the healthy networks that the underlying pathology perturbs (Archer et al., 2014). Similarly, the mechanisms of absence seizure generation are reviewed by (Carney and Jackson, 2014), revealing that it too has a signature pattern of large-scale functional brain network perturbation. The ability to make such observations has considerable clinical significance, as highlighted in the review by (Pittau et al., 2014).
The tantalising proposition that there may be a common treatment target for all focal epilepsy phenotypes is also explored in a review of the piriform cortex by (Vaughan and Jackson, 2014). The piriform cortex was first implicated as a common brain region associated with spread of interictal discharges in focal epilepsy in an experiment that analysed the spatially normalised functional imaging data of a heterogeneous group of focal epilepsy patients (Laufs et al., 2011). This finding, since replicated (Flanagan et al., 2014), led Vaughan & Jackson to explore in detail what is known of the piriform cortex. Their findings reveal the piriform has several features that likely predispose it to involvement in focal epilepsy, and features that also explain many of the peculiar symptoms experienced by patients, from olfactory auras to the characteristic nose-wiping that many patients perform postictally. This work points to the need for future studies to determine whether the piriform might be an effective target for deep brain stimulation or other targeted therapy to prevent the spread of epileptiform activity.
Original research
Temporal lobe epilepsy is investigated in several papers in this topic. One of these studies also introduces a new exploratory method, Shared and specific independent component analysis (SSICA), that builds upon independent component analysis to perform between-group network comparison (Maneshi et al., 2014). In application to mesial temporal lobe epilepsy (MTLE) and healthy controls, three distinct reliable networks were revealed: two that exhibited increased activity in patients (a network including hippocampus and amygdala bilaterally, and a network including postcentral gyri and temporal poles), and a network identified as specific to healthy controls (i.e. effectively decreased in patients, consisting of bilateral precuneus, anterior cingulate, thalamus, and parahippocampal gyrus). These finding give mechanistic clues to the cognitive impairments often reported in patients with MTLE. Further clues are revealed in a study of the dynamics of fMRI and its functional connectivity (Laufs et al., 2014). Compared to healthy controls, temporal variance of fMRI was seen to be most increased in the hippocampi of TLE patients, and variance of functional connectivity to this region was increased mainly in the precuneus, the supplementary and sensorimotor, and the frontal cortices. More severe disruption of connectivity in these networks during seizures may explain patients’ cognitive dysfunction (Laufs et al., 2014). Yang and colleagues also show that it may be possible to use fMRI functional connectivity to lateralise TLE (Yang et al., 2015), which could be a useful clinical tool.
Mechanistic explanations of symptomatology beyond the seizure onset zone can also be revealed with conventional nuclear medicine techniques such as 18F-FDG-PET. This is demonstrated in a study of Occipital Lobe Epilepsy by Wong and colleagues, who observed that patients with automatisms have metabolic changes extending from the epileptogenic occipital lobe into the ipsilateral temporal lobe, whereas in patients without automatisms the 18F-FDG-PET was abnormal only in the occipital lobe (Wong et al., 2014).
The clinical significance of the ability to non-invasively study functional brain networks extends to understanding the impact of surgery on brain networks. This Frontiers research topic includes an investigation by Doucet and colleagues revealing that temporal lobe epilepsy and surgery selectively alter the dorsal, rather than the ventral, default-mode network (Doucet et al., 2014).
Another approach to better understand the mechanisms of seizure onset and broader symptomatology is computational modelling. It can track aspects of neurophysiology than cannot be readily measured: for example effective connectivity and mean membrane potential dynamics are shown by (Freestone et al., 2014) to be estimable using model inversion. In a proof-of-principle experiment with simulated data, they demonstrate that by tailoring the model to subject-specific data, it may be possible for the framework to identify a seizure onset site and the mechanism for seizure initiation and termination. Also in this topic, Petkov and colleagues utilise a computational model of the transition into seizure dynamics to explore how conditions favourable for seizures relate to changes in functional networks. They find that networks with higher mean node degree are more prone to generating seizure dynamics in the model, thus providing a mathematical mechanistic explanation for increasing node degree causing increased ictogenicity (Petkov et al., 2014).
Seizure prediction is an area of considerable research, and in this topic Cook and colleagues reveal intriguing characteristics in the long-term temporal pattern of seizure onset. They confirmed that human inter-seizure intervals follow a power law, and they found evidence of long-range dependence. Specifically, the dynamics that led to the generation of a seizure in most patients appeared to be affected by events that took place much earlier (as little as 30 minutes prior and up to 40 days prior in some patients) (Cook et al., 2014). The authors rightly note that this information could be valuable for individually-tuned seizure prediction algorithms.
Several methodological papers in this Frontiers Topic prove there remains considerable potential to improve neuroimaging methods as applied to the study of epilepsy. For example, (Mullinger et al., 2014) reveal the critical importance of the accuracy of physical models if one is to optimise lead positioning in functional MRI with simultaneous EEG. Confirming with computer modelling and phantom measurements that lead positioning can have a substantial effect on the amplitude of the MRI gradient artefact present on the EEG, they optimised the positions in a novel cap design. However, whilst this substantially reduced gradient artefact amplitude on the phantom, it made things worse when used on human subjects. Thus, improvement is required in model accuracy if one is to make accurate predictions for the human context.
Reduction of artefact, particularly cardioballistic and non-periodic motion artefact, remains a challenge for off-the-shelf MRI-compatible EEG systems. However, for over a decade, the Jackson group in Melbourne has dealt well with this issue using insulated carbon-fibre artefact detectors, physically but not electrically attached to the scalp (Masterton et al., 2007). In the present topic, they provide detailed instructions for building such detectors and interfacing them with a commercially available MRI-compatible EEG system (Abbott et al., 2015). This team also previously developed event-related ICA (eICA), to map fMRI activity associated with inter-ictal events observed on EEG (Masterton et al., 2013b). The method is capable of distinguishing separate sub-networks characterised by differences in spatio-temporal response (Masterton et al., 2013a). The eICA approach frees one from assumptions regarding the shape of the time-course of the neuronal and haemodynamic response associated with inter-ictal activity (which can vary according to spike type, can vary from conventional models and may include pre-spike activity (Masterton et al., 2010); issues explored further in the present topic by (Faizo et al., 2014) and (Jacobs et al., 2014)). However, the effectiveness of eICA can be affected by fMRI noise or artefact. In the present topic we see that application of a fully automated de-noising algorithm (SOCK) is now recommended, as it can substantially improve the quality of eICA results (Bhaganagarapu et al., 2014).
The ability to detect activity associated with inter-ictal events can also be improved with faster image acquisition. Magnetic Resonance Encephalography (MREG) is a particularly fast fMRI acquisition method (TR=100ms) that achieves its speed using an under-sampled k-space trajectory (Assländer et al., 2013; Zahneisen et al., 2012). This has now been applied in conjunction with simultaneous EEG, to reveal that the negative fMRI response in the default-mode network is larger in temporal compared to extra-temporal epileptic spikes (Jacobs et al., 2014).
The default mode network and its relationship to epileptiform activity is also examined in several other papers in this topic. In a pilot fMRI connectivity study of Genetic Generalised Epilepsy and Temporal Lobe Epilepsy patients, (Lopes et al., 2014) observed that intrinsic connectivity in portions of the default mode network appears to increase several seconds prior to the onset of inter-ictal discharges. The authors suggest that the default mode network connectivity may facilitate IED generation. This is plausible, although causality is difficult to establish and it is possible that something else drives both the connectivity and EEG changes (Abbott, 2015).
Complicating matters further is the question of what connectivity means. There are many ways in which connectivity can be assessed. Jones and colleagues have discovered that some of these do not necessarily correlate well with each other. They examined connectivity between measurements made with intracranial electrodes, connectivity assessed using simultaneous BOLD fMRI and intracranial electrode stimulation, connectivity between low-frequency voxel measures of fMRI activity, and a diffusion MRI measure of connectivity – an integrated diffusivity measure along a connecting pathway (Jones et al., 2014). They found only mild correlation between these four measures, implying they assess quite different features of brain networks. More research in this domain would therefore be valuable.
Whatever the measure of connectivity utilised, most evidence of alterations in connectivity in epilepsy has been obtained from comparison of a group of patients with a group of healthy controls. However, a new method called Detection of Abnormal Networks in Individuals (DANI) is now proposed by (Dansereau et al., 2014). This method is designed to detect the organisation of brain activity in stable networks, which the authors call modularity. The conventional definition of modularity refers to the degree to which networks can be segregated into distinct communities, usually estimated by maximising within-group nodal links, and minimising between group links (Girvan and Newman, 2002; Rubinov and Sporns, 2010). Dansereau take a novel approach to this concept, instead evaluating the stability of each resting state network across replications of a bootstrapped clustering method (Bellec et al., 2010). In the DANI approach, the degree to which an individual’s functional connectivity modular pattern deviates from a population of controls is quantified. Whilst application of the method to epilepsy patients is preliminary, significant changes were reported likely related to the epileptogenic focus in 5 of the 6 selected focal epilepsy patients studied. In several patients, modularity changes in regions distant from the focus were also observed, adding further evidence that the pervasive network effects of focal epilepsy can extend well beyond the seizure onset zone.
When it comes to application of EEG-fMRI to detect the seizure onset zone, there is typically a trade-off between specificity and sensitivity, with the added complication that activity or network changes may also occur in brain regions other than the ictal onset zone. The distant activity may be due to activity propagation from the onset zone, pervasive changes in functional networks creating a ‘permissive state’, or in some cases might be the brain’s attempt to prevent seizures. Specificity and sensitivity of EEG-fMRI to detect the ictal onset zone is explored by (Tousseyn et al., 2014). They determined how rates of true and false positives and negatives varied with voxel height and cluster size thresholds, both for the full statistical parametric map, and for the single cluster that contained the voxel of maximum statistical significance. The latter conferred the advantage of reducing positives remote from the seizure onset zone. As a result, it appeared to be more robust to variations in statistical threshold than analysis of the entire map. One needs to be cautious however, given the small numbers of patients studied, and the fact that the “optimal” settings were determined using receiver operator characteristic curves of the same study data. It remains to be seen how well this might generalise to a different study.
Perhaps the greatest potential for future advancement in EEG-fMRI is in methods to make the most of the all the information captured by each modality. This is highlighted by the work of Deligianni et al, demonstrating with a novel analysis framework the potential to obtain more information on the human functional connectome by utilising EEG and fMRI together (Abbott, 2016; Deligianni et al., 2014).
We hope that you enjoy this collection of papers providing a broad snapshot of advances in brain mapping methods and application to better understand epilepsy.

via Frontiers | Editorial: Functional brain mapping of epilepsy networks: methods and applications | Neuroscience

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[ARTICLE] Novel gait training alters functional brain connectivity during walking in chronic stroke patients: a randomized controlled pilot trial – Full Text

Abstract

Background

A recent study has demonstrated that a turning-based treadmill program yields greater improvements in gait speed and temporal symmetry than regular treadmill training in chronic stroke patients. However, it remains unknown how this novel and challenging gait training shapes the cortico-cortical network and cortico-spinal network during walking in chronic stroke patients. The purpose of this study was to examine how a novel type of gait training, which is an unfamiliar but effective task for people with chronic stroke, enhances brain reorganization.

Methods

Subjects in the experimental and control groups received 30 min of turning-based treadmill training and regular treadmill training, respectively. Cortico-cortical connectivity and cortico-muscular connectivity during walking and gait performance were assessed before and after completing the 12-session training.

Results

Eighteen subjects (n = 9 per group) with a mean age of 52.5 ± 9.7 years and an overground walking speed of 0.61 ± 0.26 m/s consented and participated in this study. There were significant group by time interactions for gait speed, temporal gait symmetry, and cortico-cortical connectivity as well as cortico-muscular connectivity in walk-related frequency (24–40 Hz) over the frontal-central-parietal areas. Compared with the regular treadmill training, the turning-based treadmill training resulted in greater improvements in these measures. Moreover, the increases in cortico-cortical connectivity and cortico-muscular connectivity while walking were associated with improvements in temporal gait symmetry.

Conclusions

Our findings suggest this novel turning-based treadmill training is effective for enhancing brain functional reorganization underlying cortico-cortical and corticomuscular mechanisms and thus may result in gait improvement in people with chronic stroke.

Introduction

A recent study suggested that chronic stroke patients maintain the capacity to increase synchronization of neural activity between different brain regions as measured by EEG connectivity. These changes of functional connectivity in the motor cortex through neurofeedback correlate with improvements in motor performance [1]. Previously, we demonstrated that a novel specific training, the turning-based treadmill program, yielded greater improvements in gait speed and temporal symmetry than regular treadmill training for people with chronic stroke [2]. We presumed the turning-based treadmill training, which is a challenging and unfamiliar training task for chronic stroke patients, may facilitate brain reorganization and behavioral recovery [3]. Thus, we sought to understand how such novel gait training promotes brain reorganization in this study.

An EEG-based method has the advantage of real-time recording during walking due to the relative ease of data acquisition. As indicated by the authors of the first study to use an EEG signal recorded during walking, the power increases within numerous frequency bands (3–150 Hz) in the sensorimotor cortex and is more pronounced during the end of the stance phase of walking [4]. Source localization EEG analysis revealed the importance of the primary somatosensory, somatosensory association, primary motor and cingulate cortex in gait control [5]. Focal lesions due to stroke may not only affect the functional connectivity of cortical areas [6] but also impede the neural transmission of descending motor pathways [7]. Based on spectral analysis, the direct relationship of cortical activities with peripheral movements is still unknown. Accordingly, an analysis of EEG-EMG coherence recorded during treadmill walking was done by Petersen et al. [8], who demonstrated that cortical activity in the primary motor cortex within the gamma band (24–40 Hz) was transmitted via the corticospinal tract to the leg muscles during the swing phase of walking. In addition, a recent study confirmed the strong correlation between kinematic errors of the lower extremities and fronto-centroparietal connectivity during gait training and post-training in healthy subjects [9]. However, it remains unknown how novel and challenging gait training shapes the cortico-cortical network and cortico-spinal network during walking in individuals with chronic stroke. Therefore, the aims of the current study were to explore the effects of the turning-based treadmill training, a novel gait training program, on cortico-cortical connectivity and corticomuscular connectivity and to investigate the relationship between connectivity changes and gait performance in chronic stroke patients.[…]

 

Continue —> Novel gait training alters functional brain connectivity during walking in chronic stroke patients: a randomized controlled pilot trial | Journal of NeuroEngineering and Rehabilitation | Full Text

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[Abstract] Mozart’s music and multidrug-resistant epilepsy: a potential EEG index of therapeutic effectiveness.

Multidrug-resistant epilepsy is a pathological condition that affects approximately one-third of patients with epilepsy, especially those with associated intellectual disabilities. Several non-pharmacological interventions have been proposed to improve quality of life of these patients. In particular, Mozart’s sonata for two pianos in D major, K448, has been shown to decrease interictal electroencephalography (EEG) discharges and recurrence of clinical seizures in these patients. In a previous study we observed that in institutionalized subjects with severe/profound intellectual disability and drug-resistant epilepsy, a systematic music listening protocol reduced the frequency of seizures in about 50% of cases. This study aims to assess electroencephalography as a quantitative (qEEG) predictive biomarker of effectiveness of listening to music on the frequency of epileptic discharges and on background rhythm frequency (BRF).

via Mozart’s music and multidrug-resistant epilepsy: a potential EEG index of therapeutic… – Abstract – Europe PMC

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[Abstract] Seizure prediction — ready for a new era

Abstract

Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.

 

Key points

  • One clinical trial has shown that prospective seizure prediction in humans is possible.
  • Databases of EEG data provide a standard reference for comparison of seizure prediction algorithms and for hypothesis generation.
  • Competitions provide a platform for identification of the best seizure prediction algorithms.
  • The network theory of epilepsy, multimodal recording techniques, long-term monitoring and computational modelling are providing new approaches to seizure prediction.
  • The field is ready for a large-scale clinical trial of seizure prediction.

 

via Seizure prediction — ready for a new era | Nature Reviews Neurology

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[ARTICLE] Effects of 8-week sensory electrical stimulation combined with motor training on EEG-EMG coherence and motor function in individuals with stroke – Full Text

 

Abstract

The peripheral sensory system is critical to regulating motor plasticity and motor recovery. Peripheral electrical stimulation (ES) can generate constant and adequate sensory input to influence the excitability of the motor cortex. The aim of this proof of concept study was to assess whether ES prior to each hand function training session for eight weeks can better improve neuromuscular control and hand function in chronic stroke individuals and change electroencephalography-electromyography (EEG-EMG) coherence, as compared to the control (sham ES). We recruited twelve subjects and randomly assigned them into ES and control groups. Both groups received 20-minute hand function training twice a week, and the ES group received 40-minute ES on the median nerve of the affected side before each training session. The control group received sham ES. EEG, EMG and Fugl-Meyer Assessment (FMA) were collected at four different time points. The corticomuscular coherence (CMC) in the ES group at fourth weeks was significantly higher (p = 0.004) as compared to the control group. The notable increment of FMA at eight weeks and follow-up was found only in the ES group. The eight-week rehabilitation program that implemented peripheral ES sessions prior to function training has a potential to improve neuromuscular control and hand function in chronic stroke individuals.

Introduction

Stroke is one of the leading contributing factors to the loss of functional abilities and independence in daily life in adults1. The most common and widely observed impairment following stroke is motor impairment, which can be regarded as a loss or limitation of function in muscle control or movement2,3,4,5. Most stroke survivors later regain the ability to walk independently, but only fewer than 50% of them will have fully recovered upper extremity functions6,7. From a review focusing on motor recovery after stroke, it has been indicated that the recovery of both arm and hand function among subacute and chronic stroke survivors is limited in current neural rehabilitation settings4; therefore, additional management with activating plasticity before or during performing motor training is necessary for better motor recovery.

The fundamental principle of stroke rehabilitation is inducing brain plasticity by sensory or proprioceptive input in order to facilitate motor functions8,9. It has been demonstrated that strong sensory input can induce plastic changes in the motor cortex via direct or indirect pathways10,11,12,13,14,15,16,17. In this case, electrical stimulation (ES) that provides steady and adequate somatosensory input can be an ideal method of stimulating the motor cortex.

Recent studies using functional magnetic resonance imaging (fMRI) or transcranial magnetic stimulation (TMS) suggest that ES on peripheral nerves can increase motor-evoked potential (MEP)18,19,20, increase the active voxel count in the corresponding motor cortex13, and increase blood-oxygen-level dependent (BOLD) signals in fMRI, suggesting peripheral ES induced higher excitability and activation level of cortical neurons21. Since the expansion of the motor cortical area or increase in the excitability of neural circuits is associated with learning new motor skills22,23,24,25,26, clinicians should take advantage and assist patients with stroke on motor tasks training during this period of time. Celnik and colleagues27 found that the hand function of chronic stroke subjects improved immediately after two-hour peripheral nerve stimulation combined with functional training, and the effect lasted for one day. Based on previous studies, the ES that increases corticomuscular excitability may turn out to be an ideal intervention added prior to traditional motor training to “activate” the neural circuit, so that patients may get the most out of the training. According to a recent study that applied single session peripheral ES on post-stroke individuals, the corticomuscular coherence (CMC), which is the synchronization level between EEG and EMG, increased significantly and was accompanied by improvement in the steadiness of force output28.

To our knowledge, however, there is no study investigating the long-term effect of ES combined with functional training on both motor performance and cortical excitability. We targeted the median nerve because its distribution covered the dorsal side of index, middle, and half of ring finger and the palmar side of the first three fingers and half of the ring finger. Besides, median nerve is in charge of the flexion of the first three fingers, which combined they accounts for most of the functional tasks of hand. Therefore, the purpose of this pilot study was to preliminarily evaluate the effect of eight-week ES-combined hand functional training among chronic stroke patients based on CMC and motor performance. We followed up for four weeks after the intervention ceased and examined the lasting effect. We hypothesized that those who received intervention with ES would have better hand function and higher CMC than those who received intervention with sham ES. We also hypothesized that the effect would last for at least four weeks during our follow-up.[…]

Continue —> Effects of 8-week sensory electrical stimulation combined with motor training on EEG-EMG coherence and motor function in individuals with stroke | Scientific Reports

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