Charles Markham

On the suitability of near-infrared (NIR) systems for next-generation brain–computer interfaces

A brain-computer interface (BCI) gives those suffering from neuromuscular impairments a means to interact and communicate with their surrounding environment. A BCI translates physiological signals, typically electrical, detected from the brain to control an output device. A significant problem with current BCIs is the lengthy training periods involved for proficient usage, which can often lead to frustration and anxiety on the part of the user. Ultimately this can lead to abandonment of the device. The primary reason for this is that relatively indirect measures of cognitive function, as can be gleaned from the electroencephalogram (EEG), are harnessed. A more suitable and usable interface would need to measure cognitive function more directly. In order to do this, new measurement modalities, signal acquisition and processing, and translation algorithms need to be addressed. In this paper, we propose a novel approach, using non-invasive near-infrared imaging technology to develop a user-friendly optical BCI. As an alternative to the traditional EEG-based devices, we have used practical non-invasive optical techniques to detect characteristic haemodynamic responses due to motor imagery and consequently created an accessible BCI that is simple to attach and requires little user training.

Hemodynamics for Brain-Computer Interfaces

This article brings together the various elements that constitute the signal processing challenges presented by a hemodynamics-driven functional near-infrared spectroscopy (fNIRS) based brain-computer interface (BCI). We discuss the use of optically derived measures of cortical hemodynamics as control signals for next generation BCIs. To this end we present a suitable introduction to the underlying measurement principle, we describe appropriate instrumentation and highlight how and where performance improvements can be made to current and future embodiments of such devices. Key design elements of a simple fNIRS-BCI system are highlighted while in the process identifying signal processing problems requiring improved solutions and suggesting methods by which this might be accomplished.

A person independent system for recognition of hand postures used in sign language

We present a novel user independent framework for representing and recognizing hand postures used in sign language. We propose a novel hand posture feature, an eigenspace Size Function, which is robust to classifying hand postures independent of the person performing them. An analysis of the discriminatory properties of our proposed eigenspace Size Function shows a significant improvement in performance when compared to the original unmodified Size Function. We describe our support vector machine based recognition framework which uses a combination of our eigenspace Size Function and Hu moments features to classify different hand postures. Experiments, based on two different hand posture data sets, show that our method is robust at recognizing hand postures independent of the person performing them. Our method also performs well compared to other user independent hand posture recognition systems.

Physiological noise in near-infrared spectroscopy: implications for optical brain computer interfacing

Near-infrared spectroscopy is a non-invasive optical method used to detect functional activation of the cerebral cortex. Cognitive, visual, auditory and motor tasks are among the functions that have been investigated by this technique in the context of optical brain computer interfacing. In order to determine whether the optical response is due to a stimulus, it is essential to identify and reduce the effects of physiological noise. This paper characterizes noise typically present in optical responses and reports signal processing approaches used to overcome such noise.

Development of a wearable motion capture suit and virtual reality biofeedback system for the instruction and analysis of sports rehabilitation exercises

This paper describes the design and development of a computer game for instructing an athlete through a series of prescribed rehabilitation exercises. In an attempt to prevent or treat musculoskeletal type injuries along with trying to improve physical performance, athletes are prescribed exercise programmes by appropriately trained specialists. Typically athletes are shown how to perform each exercise in the clinic following examination but they often have no way of knowing if their technique is correct while they are performing their home exercise programme. We describe a system that allows an automatic audit of this activity. Our system utilises ten inertial motion tracking sensors incorporated in a wearable body suit which allows a Bluetooth connection from a root hub to a laptop/computer. Using our specifically designed software programme, the athlete can be instructed and analysed as he/she performs the individually tailored exercise programme and a log is recorded of the time and performance level of each exercise completed. We describe a case study that illustrates how a clinician can at a later date review the athletes progress and subsequently alter the exercise programme as they see fit.

Weakly Supervised Training of a Sign Language Recognition System Using Multiple Instance Learning Density Matrices

A system for automatically training and spotting signs from continuous sign language sentences is presented. We propose a novel multiple instance learning density matrix algorithm which automatically extracts isolated signs from full sentences using the weak and noisy supervision of text translations. The automatically extracted isolated samples are then utilized to train our spatiotemporal gesture and hand posture classifiers. The experiments were carried out to evaluate the performance of the automatic sign extraction, hand posture classification, and spatiotemporal gesture spotting systems. We then carry out a full evaluation of our overall sign spotting system which was automatically trained on 30 different signs.

A CMOS Camera-Based Pulse Oximetry Imaging System

In this paper a CMOS camera-based system for non-contact pulse oximetry imaging in transmission mode is described. Attention is drawn to the current uses of conventional pulse oximetry and the potential application of pulse oximetry imaging to developing objective wound assessment systems

A framework for continuous multimodal sign language recognition

We present a multimodal system for the recognition of manual signs and non-manual signals within continuous sign language sentences. In sign language, information is mainly conveyed through hand gestures (Manual Signs). Non-manual signals, such as facial expressions, head movements, body postures and torso movements, are used to express a large part of the grammar and some aspects of the syntax of sign language. In this paper we propose a multichannel HMM based system to recognize manual signs and non-manual signals. We choose a single non-manual signal, head movement, to evaluate our framework when recognizing non-manual signals. Manual signs and non-manual signals are processed independently using continuous multidimensional HMMs and a HMM threshold model. Experiments conducted demonstrate that our system achieved a detection ratio of 0.95 and a reliability measure of 0.93.

A 12-Channel, real-time near-infrared spectroscopy instrument for brain-computer interface applications

A continuous wave near-infrared spectroscopy (NIRS) instrument for brain-computer interface (BCI) applications is presented. In the literature, experiments have been carried out on subjects with such motor degenerative diseases as amyotrophic lateral sclerosis, which have demonstrated the suitability of NIRS to access intentional functional activity, which could be used in a BCI as a communication aid. Specifically, a real-time, multiple channel NIRS tool is needed to realise access to even a few different mental states, for reasonable baud rates. The 12-channel instrument described here has a spatial resolution of 30mm, employing a flexible software demodulation scheme. Temporal resolution of ∼100ms is maintained since typical topographic imaging is not needed, since we are only interested in exploiting the vascular response for BCI control. A simple experiment demonstrates the ability of the system to report on haemodynamics during single trial mental arithmetic tasks. Multiple trial averaging is not required.

Distributed Monte Carlo simulation of light transportation in tissue

A distributed Monte Carlo simulation which models the propagation of light through tissue has been developed. It allows for improved calibration of medical imaging devices for investigating tissue oxygenation in the white matter of the cerebral cortex. The application can distribute the simulation over an unbounded number of processors in parallel. We have found that this application is highly parallelisable resulting in up to 91% efficiency at 60 processors running on a homogeneous Java distributed system. A distributed system with 150 heterogeneous processors was used to simulate the paths of photons in a brain tissue model. We found that the source illumination footprint has an effect on the distribution of photons in the head and that lasers do produce a small beam in a highly scattering medium. This application will help researchers to improve the accuracy of their experiments