Posts Tagged electroencephalogram

[BLOG POST] Mozart and epilepsy: the rhythm beats on

 

I can’t seem to get away from the theme of Mozart and epilepsy. When I first looked at this, in a blog post titled Mozart and seizures? The links between epilepsy and music, I took the topic rather lightly, more a subscript than a headline you may say. But I have since learnt to take the links between epilepsy and music more seriously.

By Barbara Krafft – The Bridgeman Art Library, Object 574471, Public Domain, Link

 

The major trigger for my ‘road to Damascus’ conversion is a 2018 paper titled Study of the Mozart effect in children with epileptic electroencephalograms, published in the journal Seizure. The paper was an eye-opener because it gave a very helpful comprehensive context to the broader beneficial effect of music…not just in epilepsy, but in other neurological disorders such as Parkinson’s diseasedementia and sleep disordersThe authors, Elyza Grylls and colleagues, started on the established premise that Mozart’s music has a beneficial effect on epilepsy. What they wanted to know was if other forms of music have a similar settling effect on epilepsy, or if only Mozart’s music carries the magic touch. The authors therefore played Mozart’s Sonata for two pianos in D major (K448) to 40 children with epilepsy who were undergoing an EEG (electroencephalogram, or electrical brain wave test). They then compared this with the effect of playing other types of music. Remarkably, they found that only Mozart’s Sonata led to a significant reduction in EEG epileptic discharges.

Public Domain, Link

The authors concluded that there was indeed an anti-epileptic effect of Mozart’s music, the so-called  ‘Mozart therapy’. But what is so special about K448? They speculate that it has to do with the structure of Mozart’s music, containing as it does, long periodicities. Interestingly, the music of Yanni, which is similarly structured, has somewhat a similar effect on brain wave activity. On the contrary, and sorry to Beethoven fans, Fur Elise doesn’t have this effect.

By W.J. Baker (held the expired copyright on the photograph) – Library of Congress[1]Contrairement à une erreur fréquemment répandue le buste a été réalisé par Hugo Hagen, non pas à partir du masque mortuaire mais, comme de nombreux autres, d’après le masque réalisé en 1812 par Franz Klein pour un buste qu’il devait réaliser ensuite., Public Domain, Link

So what does the structure of Mozart’s music do to the brain? One suggestion is that Mozart’s music enhances the body’s parasympathetic drive; this reduces the heart rate, and thereby inhibits the brain’s propensity to epileptic seizures. The suppression of this parasympathetic drive is of course the theory behind using vagus nerve stimulation (VNS) to treat drug-resistant epilepsy. For more on VNS, see my previous blog, Vagus nerve stimulation: from neurology and beyond!

By Bionerd – MRI at Charite Mitte, Berlin (used with permission), CC BY 3.0Link

You have surely wondered by now if K448 is the only one of Mozart’s compositions to have an anti-epileptic effect. It doesn’t matter if you have not, because the authors of another interesting paper did. They titled their study, published in 2018, Mozart’s music in children with drug-refractory epileptic encephalopathies: comparison of two protocols. Published in the journal Epilepsy and Behaviour, the authors, Giangennaro Coppola and colleagues, compared the effect of K448 with a set of his other compositions. Intriguingly they found that the composition set actually had a greater effect in epilepsy than K448…by a wide margin of 70% to 20%! Furthermore, the set was better tolerated by the children; they were less irritable and had a better nighttime sleep quality.   

https://www.publicdomainpictures.net/en/view-image.php?image=76907&picture=dog-amp-child-painting

It therefore appears as if it all rosy in the garden of music and the brain. But it is not! As every rose grows on a thorny tree, so do some forms of music trigger epileptic seizures. This so-called musicogenic epilepsy is well-recognised, and two recent culprits are the music of Sean Paul, discussed in the journal Scientific American , and the music of Ne Yo, explored by NME. Therefore you should craft your playlist wisely.

By CLASSICNEYO – Own workCC BY-SA 4.0Link

So, is it time for neurologists to start prescribing music?

Or is it too much of a double-edged sword?

Music is #SimplyIrresistible. Luca Florio on Flickr. https://www.flickr.com/photos/elle_florio/29516744480

via Mozart and epilepsy: the rhythm beats on – The Neurology Lounge

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[Abstract + References] Classifying Imaginary Hand Movement through Electroencephalograph Signal for Neuro-rehabilitation

Abstract

Brain-Computer-Interface (BCI) has been widely used in the field of neuro-rehabilitation such as automatic controls based on brain commands to upper and lower extremity prosthesis devices in patients with paralysis. In a post-stroke period, approximately 50% of stroke sufferers have unilateral motor deficits leading to a chronic decline in chronic upper extremity function. Stroke affects patients in their productive and elderly age which is potentially creating new problems in national health development. BCI can be used to aid post-stroke patient recovery, thus motion detection and classification is essential for optimizing BCI device control. Therefore, this study aims to distinguish several hand functions such as grasping, pinching, and hand lifting from releasing movement in accordance with the usual movements performed during post-stroke rehabilitation based on brain signals obtained from electroencephalogram (EEG). In this study, the information that obtained from the processing of EEG signals were be used as inputs for artificial neural networks then classified to distinguish two types of imaginary hand movements (grasping v. releasing, pinching v. releasing, hand lifting v. releasing). The results of these classifications using Extreme Learning Machine (ELM) based on spectral analysis and CSP (Common Spatial Pattern) calculation show that ELM and CSP was a good feature in distinguishing two types of motion with software/system accuracy average above 95%. This could be useful for optimizing BCI devices in neuro-rehabilitation, such as combining with Functional Electrical Stimulator (FES) device as a self-therapy for post-stroke patient.

References

Badan Penelitian dan Pengembangan Kesehatan. Riset Kesehatan Dasar 2013, Available at : http://www.depkes.go.id/resources/download/general/Hasil%20Riskesdas%202013.pdf, accesed February 2017.

J. A. Franck. Concise Arm and Hand Rehabilitation Approach in Stroke. vol. 3. no. 4. 2015.

N. Birbaumer. A. R. Murguialday. and L. Cohen. Brain-computer interface in paralysis. Curr. Opin. Neurol. vol. 21. no. 6. pp. 634–8. 2008.

J. J. Daly. R. Cheng. J. Rogers. K. Litinas. K. Hrovat. and M. Dohring. Feasibility of a New Application of Noninvasive Brain Computer Interface (BCI): A Case Study of Training for Recovery of Volitional Motor Control After Stroke. J. Neurol. Phys. Ther. vol. 33. no. 4. pp. 203–211. 2009.

K. K. Ang. C. Guan. K. S. Phua. C. Wang. L. Zhou. K. Y. Tang. G. J. Ephraim Joseph. C. W. K. Kuah. and K. S. G. Chua. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke.. Front. Neuroeng. vol. 7. no. July. p. 30. 2014.

E. Buch. C. Weber. L. G. Cohen. C. Braun. M. A. Dimyan. T. Ard. J. Mellinger. A. Caria. S. Soekadar. A. Fourkas. and N. Birbaumer. Think to move: A neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke. vol. 39. no. 3. pp. 910–917. 2008.

G.-B. Huang. Q. Zhu. C. Siew. G. H. Ã. Q. Zhu. C. Siew. G.-B. Huang. Q. Zhu. and C. Siew. Extreme learning machine: Theory and applications. Neurocomputing. vol. 70. no. 1–3. pp. 489–501. 2006.

Emotiv Insight User Manual. 2015, Availabe at : https://www.emotiv.com, accessed June 2017

P. Szachewicz. Classification of Motor Imagery for Brain-Computer Interfaces. p. 50. 2013.

B. Shoelson. edfRead, Available at : https://www.mathworks.com/matlabcentral/fileexchange/ 31900-edfread, accesed February 2017.

J. Ethridge and W. Weaver. Common Spatial Patterns Alogarithm. MatlabCentral. 2009. .

Q. Yuan. W. Zhou. S. Li. and D. Cai. Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res. vol. 96. no. 1–2. pp. 29–38. 2011.

G. Huang. Introduction to Extreme Learning Machines. Hands-on Work. Mach. Learn. Biomed. Informatics 2006. 2006.

M. H.. A. Samaha. and K. AlKamha. Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning. Int. J. Adv. Comput. Sci. Appl. vol. 4. no. 6. p. 6. 2013.

G. Lange. C. Y. Low. K. Johar. F. A. Hanapiah. and F. Kamaruzaman. Classification of Electroencephalogram Data from Hand Grasp and Release Movements for BCI Controlled Prosthesis. Procedia Technol. vol. 26. pp. 374–381. 2016.

X. Yong and C. Menon. EEG classification of different imaginary movements within the same limb. PLoS One. vol. 10. no. 4. pp. 1–24. 2015.

via Classifying Imaginary Hand Movement through Electroencephalograph Signal for Neuro-rehabilitation | Rahma | Walailak Journal of Science and Technology (WJST)

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[ARTICLE] Application of P300 Event-Related Potential in Brain-Computer Interface – Full Text

Abstract

The primary purpose of this chapter is to demonstrate one of the applications of P300 event-related potential (ERP), i.e., brain-computer interface (BCI). Researchers and students will find the chapter appealing with a preliminary description of P300 ERP. This chapter also appreciates the importance and advantages of noninvasive ERP technique. In noninvasive BCI, the P300 ERPs are extracted from brain electrical activities [electroencephalogram (EEG)] as a signature of the underlying electrophysiological mechanism of brain responses to the external or internal changes and events. As the chapter proceeds, topics are covered on more relevant scholarly works about challenges and new directions in P300 BCI. Along with these, articles with the references on the advancement of this technique will be presented to ensure that the scholarly reviews are accessible to people who are new to this field. To enhance fundamental understanding, stimulation as well as signal processing methods will be discussed from some novel works with a comparison of the associated results. This chapter will meet the need for a concise and practical description of basic, as well as advanced P300 ERP techniques, which is suitable for a broad range of researchers extending from today’s novice to an experienced cognitive researcher.

1. Introduction

Human brain is the most complex organ of the body and it is at the center of the driving block of human nervous system. In fact, more than 100 billion nerve cells are interconnected to build the functionality of human brain. Such a complicated architecture allows the brain to control the body as well as carry out the executive functions, such as making reasons, processing thoughts, and planning for next tasks. Interestingly, electrophysiology and hemodynamic response are the two techniques that have been used to study this complex organ to understand the mechanism the brain applies to finish works. Typically, electrophysiological measurements are performed by placing electrodes or sensors on the biological tissue [12]. In neuroscience and neuro-engineering, the electrophysiological techniques are used for studying electrical properties by measuring the electrical activities of neurons in the form of electroencephalogram (EEG). EEG may be measured by two different approaches: invasive and noninvasive. Invasive procedures need a surgery to place the EEG sensor deep under the scalp. In comparison, noninvasive procedure places the electrodes on the scalp. One of the ways to study the brain is to stimulate it by presenting a paradigm.

The event-related potential (ERP) was first reported by Sutton [3]. An ERP is an electrophysiological response or electrocortical potentials triggered by a stimulation and firing of neurons. A specific psychological event or a sensor can be employed to generate the stimulation. In general, visual, auditory, and tactile are three major sources of ERP stimulation. For instance, ERP can be elicited by a surprise appearance of a character on a visual screen, or a “novel” tone presented over earphones, or by sudden pressing of a button by the subject, including myriad of other events. Presented stimulus generates a detectable but time-delayed electrical wave in EEG. EEG is recorded starting from the time of presenting the stimulus to the time when EEG settles down. Depending on the necessity, simple detection method such as ensemble averaging or advanced processes such as linear discriminant analysis or support vector machine algorithms are applied on EEG to measure the ERP. This chapter discusses the application of ERP in brain-computer interface (BCI) where P300 wave is of particular interest. ERP is time-locked to an event and appears as a series of positive and negative voltage fluctuation in the EEG that is referred to as P300 components.

2. P300 waveform

P300 is a form of visually evoked potential (VEP) and P300 ERP is embedded within the EEG signal recordable from the scalp of human brain. Depending on the components appearance following the eliciting event, the P300 can be divided into exogenous and endogenous. Early (exogenous) components are distributed over first 150 ms, whereas longer latency (endogenous) components elicit after 150 ms. Although the P300 positive deflection occurs in the EEG about 300 ms after an eliciting stimulus is delivered (which is the major reason it is termed as P300), latency can be within the range from 250 to 750 ms.

Although the actual origin of the P300 is still unclear, it is suggested that P300 is elicited by the decision making or learning that a rare event has occurred, and some things appear to be learned if and only if they are surprising [4]. The variable latency is associated with the difficulty of the decision making. In addition, the largest P300 responses are obtained over parietal zone of human head while it is attenuated with the electrodes that are gradually placed farther from this area.

To generate the P300 ERP, three different types of paradigms are being used: (1) single-stimulus, (2) oddball, and (3) three-stimulus paradigm. In each case, the subject is instructed to follow the occurrence of the target by pressing a button or mentally counting [5]. Figure 1 presents these paradigms [56]. The single-stimulus paradigm irregularly presents just one type of stimuli or target with zero occurrence of any other type of target. A typical oddball paradigm can be presented to the subject with a computer screen, a group of light-emitting diodes (LEDs), or other medium to generate a sequence of events that can be categorized into two classes: frequently presented standard (nontarget or irrelevant) and rarely presented target stimuli [7]. In an oddball paradigm, two events are presented with different probabilities in a random order, but only the irregular and rare event (the oddball event) embosses the P300 peak into the EEG about 300 ms after the stimulus onset. The three-stimulus paradigm is a modified oddball task which includes nontarget distractor (infrequent nontarget) stimuli in addition to target and standard stimuli. The distractor elicits P3a which is large over the frontal/central area [8]. In contrast, target elicits a P3b (P300), which is maximum over the parietal electrode sites. Though P3a and P3b are subcomponents of P300, P3a is dominant in the frontal/central lobe with a shorter latency and habituates faster [9].

media/F1.png

FIGURE 1.
Schematic account of three paradigms: single-stimulus (top), oddball (middle), and three-stimulus (bottom). Elicited ERP is presented at right (adapted from Ref. [5]).

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Continue —>  Application of P300 Event-Related Potential in Brain-Computer Interface | InTechOpen

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[Abstract+References] High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports 

Motor imagery (MI) with neurofeedback has been suggested as promising for motor recovery after stroke. Evidence suggests that regular training facilitates compensatory plasticity, but frequent training is difficult to integrate into everyday life. Using a wireless electroencephalogram (EEG) system, we implemented a frequent and efficient neurofeedback training at the patients’ home. Aiming to overcome maladaptive changes in cortical lateralization patterns we presented a visual feedback, representing the degree of contralateral sensorimotor cortical activity and the degree of sensorimotor cortex lateralization. Three stroke patients practiced every other day, over a period of 4 weeks. Training-related changes were evaluated on behavioral, functional, and structural levels. All 3 patients indicated that they enjoyed the training and were highly motivated throughout the entire training regime. EEG activity induced by MI of the affected hand became more lateralized over the course of training in all three patients. The patient with a significant functional change also showed increased white matter integrity as revealed by diffusion tensor imaging, and a substantial clinical improvement of upper limb motor functions. Our study provides evidence that regular, home-based practice of MI neurofeedback has the potential to facilitate cortical reorganization and may also increase associated improvements of upper limb motor function in chronic stroke patients.

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Source: High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case ReportsClinical EEG and Neuroscience – Catharina Zich, Stefan Debener, Clara Schweinitz, Annette Sterr, Joost Meekes, Cornelia Kranczioch, 2017

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[ARTICLE] Quantification of task-dependent cortical activation evoked by robotic continuous wrist joint manipulation in chronic hemiparetic stroke – Full Text

Abstract

Background

Cortical damage after stroke can drastically impair sensory and motor function of the upper limb, affecting the execution of activities of daily living and quality of life. Motor impairment after stroke has been thoroughly studied, however sensory impairment and its relation to movement control has received less attention. Integrity of the somatosensory system is essential for feedback control of human movement, and compromised integrity due to stroke has been linked to sensory impairment.

Methods

The goal of this study is to assess the integrity of the somatosensory system in individuals with chronic hemiparetic stroke with different levels of sensory impairment, through a combination of robotic joint manipulation and high-density electroencephalogram (EEG). A robotic wrist manipulator applied continuous periodic disturbances to the affected limb, providing somatosensory (proprioceptive and tactile) stimulation while challenging task execution. The integrity of the somatosensory system was evaluated during passive and active tasks, defined as ‘relaxed wrist’ and ‘maintaining 20% maximum wrist flexion’, respectively. The evoked cortical responses in the EEG were quantified using the power in the averaged responses and their signal-to-noise ratio.

Results

Thirty individuals with chronic hemiparetic stroke and ten unimpaired individuals without stroke participated in this study. Participants with stroke were classified as having severe, mild, or no sensory impairment, based on the Erasmus modification of the Nottingham Sensory Assessment. Under passive conditions, wrist manipulation resulted in contralateral cortical responses in unimpaired and chronic stroke participants with mild and no sensory impairment. In participants with severe sensory impairment the cortical responses were strongly reduced in amplitude, which related to anatomical damage. Under active conditions, participants with mild sensory impairment showed reduced responses compared to the passive condition, whereas unimpaired and chronic stroke participants without sensory impairment did not show this reduction.

Conclusions

Robotic continuous joint manipulation allows studying somatosensory cortical evoked responses during the execution of meaningful upper limb control tasks. Using such an approach it is possible to quantitatively assess the integrity of sensory pathways; in the context of movement control this provides additional information required to develop more effective neurorehabilitation therapies.

Background

The cerebral cortex plays an important role in feedforward (i.e. voluntary motor drive) and feedback control (i.e. reflexes and modulation of spinal reflexes) of human movement [1]. Cortical damage after stroke impairs both feedforward and feedback control. Altered feedforward control after stroke has been thoroughly studied and may lead to motor impairments such as weakness and abnormal synergy-dependent motor control [23].

Cortical involvement in feedback control (including sensorimotor integration and spinal reflex modulation) requires connectivity between somatosensory receptors in the periphery and the sensorimotor cortex, yet compromised integrity of this somatosensory system after stroke has received little attention in the literature. Understanding the impact of sensory impairment, as well as motor impairment, is highly relevant for the development and selection of neurorehabilitation therapies aimed to enhance and normalize motor control [4567] and for evaluating their effectiveness.

Proprioceptive and tactile information are required for feedback control of a joint, and can be studied in an experimental setting by disturbing the joint via a robotic manipulator during motor control tasks. This robotic joint manipulation results in activation of spinal reflex loops [8910] as well as in activation of the somatosensory cortex via high-resolution sensory pathways [11]. However, the cortical activity evoked by joint manipulation and consequently the cortical involvement in feedback control have received less attention.

In able-bodied individuals, evoked cortical responses to robotic joint manipulation have been studied with transient [1213] and continuous disturbances [141516]. Continuous disturbances uninterruptedly provide input to the sensory system, allowing for studying movement control and somatosensory cortical activity during meaningful motor tasks. This study determines the cortical representation of afferent (proprioceptive and tactile) information in individuals with chronic hemiparetic stroke under different upper limb control conditions, relying on objective metrics derived from the electroencephalogram (EEG). Here, the goal is to quantify evoked cortical activation in individuals with chronic hemiparetic stroke, through a combination of robotic continuous joint manipulation of the paretic limb and high-density EEG. The evoked cortical activation reveals the integrity of the connections between sensory receptors in the periphery and the sensorimotor cortices.

It is hypothesized that, due to stroke-induced damage to the somatosensory system, individuals with clinically assessed proprioceptive and tactile impairment will show decreased cortical evoked responses to continuous joint manipulation in the absence of voluntary motor activity of the affected upper limb, as compared to unimpaired persons. In general, when voluntary motor activity of the affected upper limb is required, individuals with hemiparesis have been shown to recruit their contralesional brain hemisphere, i.e. ipsilateral to the movement [17181920]. It is unclear, however, what this recruitment means with regard to somatosensory (i.e. afferent) evoked cortical activity, as the anatomical pathways conducting proprioceptive and tactile information mainly connect to the contralateral hemisphere [21]; thus, increased evoked cortical activation of the ipsilateral hemisphere is not expected.

Continue —> Quantification of task-dependent cortical activation evoked by robotic continuous wrist joint manipulation in chronic hemiparetic stroke | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1 Experimental setup. a The forearm of the participant is strapped into an armrest and the hand is strapped to the handle of the robotic manipulator, requiring no hand force to hold the handle. b Visual feedback as presented to the participant. The circle and crosshairs are always visible. The yellow arrow is only visible during the active task and points up if the target torque is applied. c Close-up of the arm in the robotic manipulator. The wrist joint is aligned with the axis of the motor and is placed in the neutral angle, defined as 20° wrist flexion. d One period of the disturbance signal applied to the wrist (root-mean-square of 0.02 rad). Zero radians corresponds to the neutral angle of the wrist

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[REVIEW] Application of BCI systems in neurorehabilitation: a scoping review

Abstract

Purpose: To review various types of electroencephalographic activities of the brain and present an overview of brain–computer interface (BCI) systems’ history and their applications in rehabilitation.

Methods: A scoping review of published English literature on BCI application in the field of rehabilitation was undertaken. IEEE Xplore, ScienceDirect, Google Scholar and Scopus databases were searched since inception up to August 2012. All experimental studies published in English and discussed complete cycle of the BCI process was included in the review.

Results and discussion: In total, 90 articles met the inclusion criteria and were reviewed. Various approaches that improve the accuracy and performance of BCI systems were discussed. Based on BCI’s clinical application, reviewed articles were categorized into three groups: motion rehabilitation, speech rehabilitation and virtual reality control (VRC). Almost half of the reviewed papers (48%) concentrated on VRC. Speech rehabilitation and motion rehabilitation made up 33% and 19% of the reviewed papers, respectively. Among different types of electroencephalography signals, P300, steady state visual evoked potentials and motor imagery signals were the most common.

Conclusions: This review discussed various applications of BCI in rehabilitation and showed how BCI can be used to improve the quality of life for people with neurological disabilities. It will develop and promote new models of communication and finally, will create an accurate, reliable, online communication between human brain and computer and reduces the negative effects of external stimuli on BCI performance.

Implications for Rehabilitation

  • The field of brain–computer interfaces (BCI) is rapidly advancing and it is expected to fulfill a critical role in rehabilitation of neurological disorders and in movement restoration in the forthcoming years.
  • In the near future, BCI has notable potential to become a major tool used by people with disabilities to control locomotion and communicate with surrounding environment and, consequently, improve the quality of life for many affected persons.
  • Electrical field recording at the scalp (i.e. electroencephalography) is the most likely method to be of practical value for clinical use as it is simple and non-invasive. However, some aspects need future improvements, such as the ability to separate close imagery signal (motion of extremities and phalanges that are close together).

via Application of BCI systems in neurorehabilitation: a scoping review, Disability and Rehabilitation: Assistive Technology, Informa Healthcare.

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