Posts Tagged tibialis anterior

[ARTICLE] Speed-adaptive control of functional electrical stimulation for dropfoot correction – Full Text

Abstract

Background

Functional electrical stimulation is an important therapy technique for dropfoot correction. In order to achieve natural control, the parameter setting of FES should be associated with the activation of the tibialis anterior.

Methods

This study recruited nine healthy subjects and investigated the relations of walking speed with the onset timing and duration of tibialis anterior activation. Linear models were built for the walking speed with respect to these two parameters. Based on these models, the speed-adaptive onset timing and duration were applied in FES-assisted walking for nine healthy subjects and ten subjects with dropfoot. The kinematic performance of FES-assisted walking triggered by speed-adaptive stimulation were compared with those triggered by the heel-off event, and no-stimulation walking at different walking speeds.

Results

Higher ankle dorsiflexion angle was observed in heel-off stimulation and speed-adaptive stimulation conditions than that in no-stimulation walking condition at all the speeds. For subjects with stroke, the ankle plantarflexion angle in speed-adaptive stimulation condition was similar to that in no-stimulation walking condition, and it was significant larger than that in heel-off stimulation condition at all speeds.

Conclusions

The improvement in ankle dorsiflexion without worsening ankle plantarflexion in speed-adaptive stimulation condition could be attributed to the appropriate stimulation timing and duration. These results provide evidence that the proposed stimulation system with speed-related parameters is more physiologically appropriate in dropfoot correction, and it may have great potential value in future clinical applications.

 

Background

About three quarters of stroke survivors experience different levels of brain dysfunction and movement disorder [1], which result in lower living quality and limited ability in social activities [2]. Of these subjects, 20% suffer from impaired motor function in the lower extremities. One of such impairments is dropfoot, which is characterized by poor ankle dorsiflexion during the swing phase and an inability to achieve heel strike at the initial contact [34]. Abnormal gaits such as circumduction gait and abnormal foot clearance on the affected side are often found as a method of compensating for excessive hip abduction and pelvis elevation on the unaffected side [5]. This results in gait asymmetry and slow walking speed [6].

Functional electrical stimulation was a representative intervention to correct dropfoot and Liberson et al. first introduced functional electrical stimulation (FES) to correct dropfoot for chronic hemiplegic subjects in the 1960s [7]. An electrical charge is delivered via a pair of electrodes to activate the tibialis anterior (TA), which results in ankle dorsiflexion. Yan et al. applied two dual-channel stimulators to the quadriceps, hamstring, gastrocnemius, and TA to recover motor function of the lower extremities in an early stage after stroke [8]. The stimulation was followed by a predetermined sequence of muscle activations that mimic a healthy gait cycle [9]. The duration of stimulation was five seconds in Yan et al.’s study. However, subjects with different severities of impairment might have different walking speeds [10], which means that a fixed stimulation duration might not be able to account for different walking patterns.

Liberson et al. used the heel-off event detected by a footswitch to trigger the stimulation [7]. However, the reliability of the footswitch controller was significantly reduced when subjects who dragged their feet during walking encountered a slope or an obstacle [11]. Bhadra et al. proposed a manual switch to trigger stimulation as a walking aid for subjects with spinal cord injury (SCI) [12]. However, manual control may distract subjects from maintaining balance and lead to an increased risk of falls [1314]. Furthermore, the cable between the control sensor and stimulator was inconvenient for walking [15].

Instead of a footswitch, Mansfield et al. [16] and Monaghan et al. [17] detected the heel event of the gait cycle in FES-assisted walking using an accelerometer and a uniaxial gyroscope, respectively. The commercially available product WalkAide also uses an accelerometer for this purpose [18]. Electromyography (EMG) signal is also applied as a control source in FES-assisted walking for the detection of volitional intent of muscle [19]. Yeom et al. amplified the EMG signal of the TA and modulated the stimulation intensity in proportion to the integrated EMG envelope. The electrical pulses are then sent to the common peroneal nerve for dropfoot correction [20].

In these studies, FES applied to the TA was mainly triggered by the heel-off event. However, this event occurs during the push-off phase and before TA activation [17]. An earlier start of TA stimulation results in reduced ankle plantarflexion [21]. Spaich et al. suggested implementing a constant time interval before the onset timing of TA stimulation to extend the push-off phase before the ankle dorsiflexion [21]. Some studies have found that walking speed can affect the activation of TA [2223]. Shiavi et al. found that the duration of EMG activity decreased as speed increased [22]. In Winter et al.’s study, the shape of the EMG patterns generally remained similar at the different walking speeds and the duration of EMG activity was closely related to the normalized stride time [23]. Although the duration of TA activation changes with the walking speeds has been reported [24], the selection of speed-adaptive FES parameters for TA has not been investigated.

The objective of this study is to find a more physiologically appropriate FES design for dropfoot correction. Firstly, speed-related changes in onset timing and the duration of TA activation were examined. Next, linear models were built for the walking speed and time interval from the heel-off event to the onset timing of TA activation, as well as for the walking speed and the duration of the TA activation. The speed-adaptive stimulation (SAS) timing and duration were then calculated based on the two models and applied for FES-assisted walking. Finally, the performance of stimulation triggered by SAS, heel-off event (HOS) and no stimulation (NS) were compared during FES-assisted walking on both subjects with stroke and healthy subjects at different walking speeds.[…]

 

Continue —->  Speed-adaptive control of functional electrical stimulation for dropfoot correction | Journal of NeuroEngineering and Rehabilitation | Full Text

 

Fig. 1a The experiment setup of SAS condition; b one healthy subject on the treadmill for system evaluation; c the position of five markers on the right leg

, , , , , ,

Leave a comment

[ARTICLE] Comparison of the Efficacy of a Real-Time and Offline Associative Brain-Computer-Interface – Full Text

An associative brain-computer-interface (BCI) that correlates in time a peripherally generated afferent volley with the peak negativity (PN) of the movement related cortical potential (MRCP) induces plastic changes in the human motor cortex. However, in this associative BCI the movement timed to a cue is not detected in real time. Thus, possible changes in reaction time caused by factors such as attention shifts or fatigue will lead to a decreased accuracy, less pairings, and likely reduced plasticity. The aim of the current study was to compare the effectiveness of this associative BCI intervention on plasticity induction when the MRCP PN time is pre-determined from a training data set (BCIoffline), or detected online (BCIonline). Ten healthy participants completed both interventions in randomized order. The average detection accuracy for the BCIonline intervention was 71 ± 3% with 2.8 ± 0.7 min-1 false detections. For the BCIonline intervention the PN did not differ significantly between the training set and the actual intervention (t9 = 0.87, p = 0.41). The peak-to-peak motor evoked potentials (MEPs) were quantified prior to, immediately following, and 30 min after the cessation of each intervention. MEP results revealed a significant main effect of time, F(2,18) = 4.46, p = 0.027. The mean TA MEP amplitudes were significantly larger 30 min after (277 ± 72 μV) the BCI interventions compared to pre-intervention MEPs (233 ± 64 μV) regardless of intervention type and stimulation intensity (p = 0.029). These results provide further strong support for the associative nature of the associative BCI but also suggest that they likely differ to the associative long-term potentiation protocol they were modeled on in the exact sites of plasticity.

Introduction

Since Daly et al. (2009) proposed the possibility of a Brain-Computer-Interface (BCI) designed for neuromodulation of stroke patients, the field has rapidly expanded with numerous novel BCIs being introduced and tested in the clinic (Ang et al., 2010Broetz et al., 2010Cincotti et al., 2012Li et al., 2013Ramos-Murguialday et al., 2013Mukaino et al., 2014Young et al., 2014Pichiorri et al., 2015Mrachacz-Kersting et al., 2016). To date the main focus has been on upper limb rehabilitation with relatively few targeting lower limb function (for a review see, Teo and Chew, 2014Cervera et al., 2018). In addition, only one group has investigated patients in the sub-acute phases of stroke (Mrachacz-Kersting et al., 2017b), presumably due to the relatively stable condition that a chronic stroke patient presents. Effects from the use of a BCI are thus easier to control since patients in the acute and subacute phase are prone to spontaneous biological recovery (Krakauer and Marshall, 2015).

Typically, BCIs function by collecting the brain signals during a specific state such as performing a movement or motor imagery, extracting features of interest and then translating these into commands for external device control (Daly and Wolpaw, 2008). The available non-invasive BCIs for stroke patients have implemented both electroencephalography (EEG) or near-infrared spectroscopy (NIRS) to acquire the brain signals, extracted various spectral and temporal features [e.g., sensorimotor rhythm, movement related cortical potentials (MR)] and provided diverse types of afferent feedback to the patient such as those generated from using robotic devices, virtual reality or by driving direct nerve or muscular electrical stimulation (for review see, Cervera et al., 2018).

A vital component of any BCI designed for rehabilitation of lost motor function in stroke patients, is that the physiological theories behind learning and memory must be satisfied. One of the most influential theories was proposed in 1949 by Hebb (2005) from which we know that “Cells that fire together, wire together.” Although Hebb proposed his theory on theoretical grounds, animal data later verified that if the pre-synaptic neuron is activated simultaneously with the post-synaptic cell, plasticity is induced, often referred to as long-term potentiation (for a review see, Cooke and Bliss, 2006). In 2000, a group from Rostock University were the first to demonstrate long-term potentiation like plasticity in the intact human brain (Stefan, 2000) with later applications to lower limb muscles (Mrachacz-Kersting et al., 2007). In this intervention [paired associative stimulation (PAS)], a peripheral nerve that innervates the target muscle is activated using a single electrical stimulus and once the generated afferent volley has arrived at the motor cortex, a single non-invasive transcranial magnetic stimulus (TMS) is provided to that area of the motor cortex that has a direct connection to the target muscle (for a review see, Suppa et al., 2017).

In a modified version of PAS, the TMS stimulus has been replaced by the movement related cortical potential (MRCP) (Mrachacz-Kersting et al., 2012). The MRCP, that can be readily measured using EEG, is a slow negative potential that arises approximately 1–2 s prior to movement execution or imagination and attains its peak negativity at the time of movement execution (Walter et al., 1964). This intervention, also termed an associative BCI, induces significant plasticity of the cortical projections to the target muscle and leads to significant functional improvements in chronic and subacute stroke patients (Mrachacz-Kersting et al., 20162017b). In the first phase, patients are asked to attempt 30–50 movements (dorsiflexion of the foot), timed to a visual cue and they receive no sensory feedback. The time of the peak negativity (PN) of the resulting MRCP for every trial is extracted and an average calculated. During the second phase (the actual associative BCI intervention), this time is used to trigger the electrical stimulation of the target nerve such that the generated afferent volley arrives at the motor cortex at precisely peak negativity. Typically, 30–50 such pairings are performed over 3–12 sessions. Since the trigger of the electrical stimulator is not based on the online detection of the MRCP during the second phase, this intervention does not represent a BCI in the classical sense. In the current study the aim was to compare the effects of this associative BCI intervention on plasticity induction as quantified by the motor evoked potential (MEP) following TMS when the MRCP PN time is determined from the phase one trials (BCIoffline modus) or detected during the second phase by using the phase one trials as a training data set (BCIonline modus).[…]

 

Continue —> Frontiers | Comparison of the Efficacy of a Real-Time and Offline Associative Brain-Computer-Interface | Neuroscience

, , , , , , ,

Leave a comment

%d bloggers like this: