The Strathclyde Brain Computer Interface (S-BCI) : the road to clinical translation

In this paper, we summarise the state of development of the Strathclyde Brain Computer Interface (S-BCI) and what has been so far achieved. We also briefly discuss our next steps for translation to spinal cord injured patients and the challenges we envisage in this process and how we plan to address some of them. Projections of the S-BCI project for the coming few years are also presented

Impact of stimulus configuration on steady state visual evoked potentials (SSVEP) response

We investigate the impact of configuration of multistimuli presented in computer monitor to steady-state visual evoked potential response. The configuration of stimuli is defined by three parameters-the size of stimuli, the separation distance between the stimuli and the layout. Two 4 by 4 checkerboards in twelve configurations were presented to the subjects. 9 subjects participated in this study. Subjects’ electroencephalography (EEG) data was off-line analyzed by using Fast Fourier Transform (FFT). The mean classification rates of configuration with bigger size and larger separation distance is higher than those configurations with smaller size and shorter separation distance. These results suggest that the stimulus size is the most important parameter of three, followed by the separation distance and layout

Evaluation of the feasibility of a novel distance adaptable steady-state visual evoked potential based brain-computer interface

Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has attracted great attention in BCI research due to its advantages over the other electroencephalography (EEG) based BCI paradigms, such as high speed, high signal to noise ratio, high accuracy, commands scalability and minimal user training time. Several studies have demonstrated that SSVEP BCI can provide a reliable channel to the users to communicate and control an external device. While most SSVEP based BCI studies focus on encoding the visual stimuli, enhancing the signal detection and improving the classification accuracy, there is a need to bridge the gap between BCI "bench" research and real world application. This study proposes a novel distance adaptable SSVEP based BCI paradigm which allows its users to operate the system in a range of viewing distances between the user and the visual stimulator. Unlike conventional SSVEP BCI where users can only operate the system at a fixed distance in front of the visual stimulator, users can operate the proposed BCI at a range of viewing distances. 10 healthy subjects participated in the experiment to evaluate the feasibility of the proposed SSVEP BCI. The visual stimulator was presented to the subjects at 4 viewing distances, 60cm, 150cm, 250cm and 350cm. The mean classification accuracy across the subjects and the viewing distances is over 75 The results demonstrate the feasibility of a distance adaptable SSVEP based BCI

EEG signatures of arm isometric exertions in preparation, planning and execution

The electroencephalographic (EEG) activity patterns in humans during motor behaviour provide insight into normal motor control processes and for diagnostic and rehabilitation applications. While the patterns preceding brisk voluntary movements, and especially movement execution, are well described, there are few EEG studies that address the cortical activation patterns seen in isometric exertions and their planning. In this paper, we report on time and time-frequency EEG signatures in experiments in normal subjects (n=8), using multichannel EEG during motor preparation, planning and execution of directional centre-out arm isometric exertions performed at the wrist in the horizontal plane, in response to instruction-delay visual cues. Our observations suggest that isometric force exertions are accompanied by transient and sustained event-related potentials (ERP) and event-related (de-)synchronisations (ERD/ERS), comparable to those of a movement task. Furthermore, the ERPs and ERD/ERS are also observed during preparation and planning of the isometric task. Comparison of ear-lobe-referenced and surface Laplacian ERPs indicates the contribution of superficial sources in supplementary and pre-motor (FCz), parietal (CPz) and primary motor cortical areas (C1 and FC1) to ERPs (primarily negative peaks in frontal and positive peaks in parietal areas), but contribution of deep sources to sustained time-domain potentials (negativity in planning and positivity in execution). Transient and sustained ERD patterns in μ and β frequency bands of ear-lobe-referenced and surface Laplacian EEG indicate the contribution of both superficial and deep sources to ERD/ERS. As no physical displacement happens during the task, we can infer that the underlying mechanisms of motor-related ERPs and ERD/ERS patterns do not only depend on change in limb coordinate or muscle-length-dependent ascending sensory information and are primary generated by motor preparation, direction-dependent planning and execution of isometric motor tasks. The results contribute to our understanding of the functions of different brain regions during voluntary motor tasks and their activity signatures in EEG can shed light on the relationships between large-scale recordings such as EEG and other recordings such as single unit activity and fMRI in this context

Analysis of movement of an elbow joint with a wearable robotic exoskeleton Using OpenSim software.

Human body movement occurs as a result of a coordinated effort between the skeleton, muscles, tendons, ligaments, cartilage, and other connective tissue. The study of movement is crucial in the treatment of some neurological and musculoskeletal diseases. The advancement of science and technology has led to the development of musculoskeletal model simulation software such as OpenSim that plays a very significant role in tackling complex bioengineering challenges and assists in our understanding of human movement. Such biomechanical models of musculoskeletal systems may also facilitate medical decision-making. Through fast and accurate calculations, OpenSim modelling enables prediction and visualisation of motion problems. OpenSim has been used in many studies to investigate and assess movements of the upper limb under various scenarios. This work investigates elbow movement of a paretic arm wearing a myoelectric robotic exoskeleton. The simulation focuses on the exoskeleton elbow joint with one degree of freedom for individuals that we have developed to support and rehabilitate a weakened/paretic arm due to a spinal cord injury for example. Accordingly, it simulates the kinematic characteristics of the human arm whilst the exoskeleton assists the arm flexion/extension to maximise its range of motion. To obtain the motion data required for this study, a forward dynamics method must be implemented. Firstly, inverse kinematics is applied to the joint angles, and then, the torque and force required for angular motion of the elbow joint are calculated using forward dynamics. The results show that the muscle forces required to generate an elbow flexion are considerably less when the exoskeleton is worn. Clinical Relevance--- The exoskeleton assists patients to extend and flex their arm, thus supporting rehabilitation and arm function during activities of daily living. Exoskeleton movement is derived from residual myoelectric signals extracted from the patient's arm muscles. Modelling the dynamics and kinematics of the arm with the exoskeleton can reveal and predict any movement issues that need to be addressed

Single trial classification of EEG in predicting intention and direction of wrist movement : translation toward development of four-class brain computer interface system based on a single limb

Brain - computer interfaces (BCI) are paradigms that offer an alternative communication channel between neural activity gene rated in the brain and the users’ external environment. The aim of this paper is to investigate the feasibility of designing and developing a multiclass BCI system based on a single limb movement due to the factor, high dimensional control channels would expand the capacity of BCI application (multidimensional control of neuroprosthesis). This paper also proposes a method to identify the optimal frequency band and recording channel to achieve the best classification result . Twenty eight surface electroencephalography ( EEG ) electrodes are used to record brain activity from eleven subjects whilst imagining and performing right wrist burst point - to - point movement towards multiple directions using a high density montage with 10 - 10 electrode placement locations focusing on motor cortex areas. Two types of spatial filters namely Common average reference (CAR) and Laplacian (LAP) filter have been implemented and results are compared to enhance the EEG signal. Features are extracted from the filtered signals using event related spectral perturbation ( ERSP ) and power spectrum. Feature vectors are classified by k - nearest neighbour ( k - NN) and quadratic discriminant analysis (QDA) classifiers. The results indicate that the majority of the optimum classification results are obtained from features extracted from contralateral electrodes in the gamma band. Based on a single trial, the average of the classification accuracy using LAP filter and k - NN classifier across the subjects in predicting intention and direction of movement is 68% and 62% for motor imagery and motor performance respectively; which is significantly higher than chance. The classification result from the majority of subjects shows that, it is possible and achievable to develop multiclass BCI systems based on a single limb

Automatic misclassification rejection for LDA classifier using ROC curves

This paper presents a technique to improve the performance of an LDA classifier by determining if the predicted classification output is a misclassification and thereby rejecting it. This is achieved by automatically computing a class specific threshold with the help of ROC curves. If the posterior probability of a prediction is below the threshold, the classification result is discarded. This method of minimizing false positives is beneficial in the control of electromyography (EMG ) based upper-limb prosthetic devices. It is hypothesized that a unique EMG pattern is associated with a specific hand gesture. In reality, however, EMG signals are difficult to distinguish, particularly in the case of multiple finger motions, and hence classifiers are trained to recognize a set of individual gestures. However, it is imperative that misclassifications be avoided because they result in unwanted prosthetic arm motions which are detrimental to device controllability. This warrants the need for the proposed technique wherein a misclassified gesture prediction is rejected resulting in no motion of the prosthetic arm. The technique was tested using surface EMG data recorded from thirteen amputees performing seven hand gestures. Results show the number of misclassifications was effectively reduced, particularly in cases with low original classification accuracy

Automatic quantification of vocal cord movement symmetry based on fibre-optic nasendoscopy video processing

Automatic Quantification of Vocal Cord Movement Symmetry based on Fibre-optic Nasendoscopy Video ProcessingIntroductionClinical subjective assessment of the symmetry of vocal cord movement may lead to variability in diagnosis, particularly in challenging cases. We aimed to enhance diagnostic practices by quantifying vocal cord motion, recorded in nasendoscopy videos, using bespoke image processing to measure vocal cord movement symmetry. Materials & Methods: With patient consent, routine clinical video data of vocal cord motion were recorded using flexible fibre-optic nasendoscopy connected to a 25 frame per second camera. In this study, 3 normal cases and 3 unilateral palsies were examined. A sequence of video frames pertaining to abduction movements were manually selected and input into the custom software for automatic processing. The algorithm executes a novel framework to quantify vocal cord motion, with steps involving glottal area segmentation, estimation of motion between successive frames of delineated vocal cord edges and measurement of movement symmetry. Results: In the normal cases, the horizontal velocity of motion of one vocal cord was at least 80% (±0.58%) of the other. The unilateral paralysed vocal cords were found to achieve only 18% (severe), 58% and 59% motion of the contralateral vocal cord. Conclusion: The results demonstrate a technique to objectively quantify vocal cord movement. Future work involves a comparative study of the results with subjective clinicians’ rating of vocal cord motion, on a discrete 5-point scale. This technique may potentially be used in the diagnosis of subtle vocal cord movement asymmetries and early reduction in vocal cord movement due to pathology, and to aid in the assessment of outcomes of post-surgical interventions

Study on interaction between temporal and spatial information in classification of EMG signals in myoelectric prostheses

Advanced forearm prosthetic devices employ classifiers to recognize different electromyography (EMG) signal patterns, in order to identify the user's intended motion gesture. The classification accuracy is one of the main determinants of real-time controllability of a prosthetic limb and hence the necessity to achieve as high an accuracy as possible. In this paper, we study the effects of the temporal and spatial information provided to the classifier on its offline performance and analyze their interdependencies. EMG data associated with seven practical hand gestures were recorded from partial-hand and trans-radial amputee volunteers as well as able-bodied volunteers. An extensive investigation was conducted to study the effect of analysis window length, window overlap a nd the number of electrode channels on the classification accuracy as well as their interactions. Our main discoveries are that the effect of analysis window length on classification accuracy is practically independent of the number of electrodes for all participant groups; window overlap has no direct influence on classifier performance, irrespective of the window length, number of channels or limb condition; the type of limb deficiency and the existing channel count influence the reduction in classification error achieved by adding more number of channels; partial-hand amputees outperform trans-radial amputees, with classification accuracies of only 11.3 % below values achieved by able-bodied volunteers

Automatic quantification of vocal cord paralysis - an application of fibre-optic endoscopy video processing

Full movement of the vocal cords is necessary for life sustaining functions. To enable correct diagnosis of reduced vocal cord motion and thereby potentially enhance treatment outcomes, it is proposed to objectively determine the degree of vocal cord paralysis in contrast to the current clinical practice of subjective evaluation. Our study shows that quantitative assessment can be achieved using optical flow based motion estimation of the opening and closing movements of the vocal cords. The novelty of the proposed method lies in the automatic processing of fibre-optic endoscopy videos to derive an objective measure for the degree of paralysis, without the need for high-end data acquisition systems such as high speed cameras or stroboscopy. Initial studies with three video samples yield promising results and encourage further investigation of vocal cord paralysis using this technique