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.

Study on Interaction Between Temporal and Spatial Information in Classification of EMG Signals for 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 off-line performance and analyze their inter-dependencies.

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.

Lower arm electromyography (EMG) activity detection using local binary patterns.

This paper presents a new electromyography activity detection technique in which 1-D local binary pattern histograms are used to distinguish between periods of activity and inactivity in myoelectric signals. The algorithm is tested on forearm surface myoelectric signals occurring due to hand gestures. The novel features of the presented method are that: 1) activity detection is performed across multiple channels using few parameters and without the need for majority vote mechanisms, 2) there are no per-channel thresholds to be tuned, which makes the process of activity detection easier and simpler to implement and less prone to errors, 3) it is not necessary to measure the properties of the signal during a quiescent period before using the algorithm.

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.

EEG decoding of semantic category reveals distributed representations for single concepts.

Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining techniques that allows the category of individual concepts to be decoded from single trials of EEG data. Neural activity was recorded while participants silently named images of mammals and tools, and category could be detected in single trials with an accuracy well above chance, both when considering data from single participants, and when group-training across participants.

Human behavior integration improves classification rates in real-time BCI.

Brain-computer interfaces (BCI) offer potential for individuals with a variety of motor and sensory disabilities to interact with their environment, communicate and control mobility aids. Two key factors which affect the performance of a BCI and its usability are the feedback given to the participant and the subject's motivation. This paper presents the results from a study investigating the effects of feedback and motivation on the performance of the Strathclyde Brain Computer Interface.

The Strathclyde brain computer interface.

Brain-computer interfaces (BCI) offer potential for individuals with a variety of motor and sensory disabilities to control their environment, communicate, and control mobility aids. However, the key to BCI usability rests in being able to extract relevant time varying signals that can be classified into usable commands in real time. This paper reports the first success of the Strathclyde BCI controlling a wheelchair on-line in Virtual Reality.

Spectral analysis of category-specific knowledge in the intact brain.

This paper implements spectral analysis of scalp EEG recordings during a language naming and visualisation task. The method offers new frontier to explore spatio-temporal features of the organisation of conceptual knowledge in the intact brain. Our findings tallies with results reported in the literature using other techniques such as fMRI.

Classification of Wrist Movements using EEG-based Wavelets Features.

Our aim is to assess and evaluate signal processing and classification methods for extracting features from EEG signals that are useful in developing brain-computer interfaces. In this paper, we report on results of developing a method to classify wrist movements using EEG signals recorded from a subject whilst controlling a joystick and moving it in different directions. Such method could be potentially useful in building brain-computer interfaces (BCIs) where a paralysed person could communicate with a wheelchair and steer it to the desired direction using only EEG signals.