Brain-computer Interface For Rehabilitation Research Articles

FNIRS-EEG BCIs for Motor Rehabilitation: A Review.

Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain-computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain's state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.

New Methodology of Brain Computer Interface for Rehabilitation of Obsessive-Compulsive Disorder

This article reviews past papers about obsessive-compulsive disorder (OCD), brain-computer interface (BCI) and experimental design guidelines, then proposes a new methodology for implementing BCI as a treatment for BCI patients and predicts possible results for this method. OCD is a neuropsychiatric disorder that leads patients to perform certain compulsions that relieve their feeling of impending doom. Other symptoms include intrusive thoughts and doubts. A new methodology was proposed in this paper because the most common treatment is currently limited to medication and cognitive behavioral therapy. It is predicted that, with the methodology proposed in this paper, OCD patients will be able to have reduced symptoms and develop an increase in awareness of performing specific actions to alleviate symptoms. There are some limitations to this new methodology, as the experiment was not performed, and thus there were no actual results to back up the proposal. This method proposal is only a theoretical approach, and more research must be done before implementing BCI as a treatment for OCD.

Emotiv EPOC+ fed electrical muscle stimulation system; an inexpensive brain-computer interface for rehabilitation of neuro muscular disorders.

Advancements in the Neuro-rehabilitation across Pakistan is warranted to effectively and efficiently deal with the disease burden of neurological conditions. Being a developing country, an in-expensive treatment approach is required to culminate the rise in the disease occurrence in Pakistan. Brain-Computer Interfaces (BCIs) have come up as a new channel for communication and control, eliminating the need of physical input, opening doors to a wide array of applications in terms of assistive and rehabilitative devices for paralyzed patients and those with neuromuscular disorders. Even with a promising prospect, BCIs and electroencephalograms (EEG) can be very expensive and therefore, they are not practically applicable. For this reason, the purpose of the current study was to come up with a possibility of an inexpensive BCI for rehabilitation of patients with neuro-muscular disorders in Pakistan by using a low-cost and readily available equipment like Emotiv EPOC+ EEG headset and electrical muscle stimulator.

DESIGN OF CONTROL SYSTEM FOR MOTOR IMAGERY BASED NEURO-AID APPLICATION

The application of Brain Computer Interface (BCI) for rehabilitation purpose has gained wide popularity in recent times. BCI for rehabilitation involves detection of brain signals, when the subject performs some sort of Motor Imagery (MI) task, for example, imagination of movement of limbs. Imagination of such movement causes desynchronization of neurons of one part of the brain gets within other parts synchronized. Band power features are best suited for quantification of the synchronization phenomenon. In the present work, extreme learning machine (ELM) and support vector machine (SVM) based classifiers are used to classify the test data. The classifier output is further used to generate control signals for driving a stepper motor, which may be used to drive some neuro-aid application device. In order to achieve a workable model for pragmatic applications, it is necessary to design a robust in nature stepper motor. Open loop analysis, closed loop analysis and performance analysis of motor with possible disturbances are carried out to evaluate the effectiveness of the proposed work. The maximum accuracy using ELM and SVM classifiers are achieved as 90% and 87.78% with a training time of 0.2496[Formula: see text]s and 3.964[Formula: see text]s, respectively. In the open loop and closed loop analysis, the desired angular movement (task imagined for rehabilitation) is achieved with an accuracy of 54.14% and 93.4%, respectively. These results suggest that a BCI system can be designed with higher efficiency with the help of MI data.

Classification of different reaching movements from the same limb using EEG

Objective. Brain–computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.

Motor imagery based brain–computer interfaces: An emerging technology to rehabilitate motor deficits

When the sensory–motor integration system is malfunctioning provokes a wide variety of neurological disorders, which in many cases cannot be treated with conventional medication, or via existing therapeutic technology. A brain–computer interface (BCI) is a tool that permits to reintegrate the sensory–motor loop, accessing directly to brain information. A potential, promising and quite investigated application of BCI has been in the motor rehabilitation field. It is well-known that motor deficits are the major disability wherewith the worldwide population lives. Therefore, this paper aims to specify the foundation of motor rehabilitation BCIs, as well as to review the recent research conducted so far (specifically, from 2007 to date), in order to evaluate the suitability and reliability of this technology. Although BCI for post-stroke rehabilitation is still in its infancy, the tendency is towards the development of implantable devices that encompass a BCI module plus a stimulation system.

Interaction of BCI with the underlying neurological conditions in patients: pros and cons.

Interaction of BCI with the underlying neurological conditions in patients: pros and cons.

A Brain-Computer Interface for rehabilitation after stroke

A Brain-Computer Interface for rehabilitation after stroke