Tomas E. Ward

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.

Artifact Removal in Physiological Signals—Practices and Possibilities

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare “time bomb” has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.

The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique

Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results.

On consistency and network latency in distributed interactive applications: a survey--part I

This paper is the first part of a two-part paper that documents a detailed survey of the research carried out on consistency and latency in distributed interactive applications (DIAs) in recent decades. Part I reviews the terminology associated with DIAs and offers definitions for consistency and latency. Related issues such as jitter and fidelity are also discussed. Furthermore, the various consistency maintenance mechanisms that researchers have used to improve consistency and reduce latency effects are considered. These mechanisms are grouped into one of three categories, namely time management, information management, and system architectural management. This paper presents the techniques associated with the time management category. Examples of such mechanisms include time warp, lock step synchronization, and predictive time management. The remaining two categories are presented in part II of the survey.

Medical applications of terahertz imaging: a review of current technology and potential applications in biomedical engineering

Terahertz (THz) imaging is in its early stages of development but already the potential clinical impact of this new imaging modality is clear. From cancer research to DNA analysis THz technology is improving or even making possible imaging of hitherto inaccessible phenomena. In this paper we present a short review of THz imaging from the point of view of biomedical engineering. We discuss the current state of the art in terms of THz imaging systems; describe current applications, future potential and our own approaches to harnessing this novel technology. We draw attention to open problems in the area with respect to the limitations of the technology before concluding with descriptions of our future work in the area.

A Methodology for Validating Artifact Removal Techniques for Physiological Signals

Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a “ground truth” signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this “ground truth,” together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform.

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.

NIRS-measured prefrontal cortex activity in neuroergonomics: strengths and weaknesses

Contemporary daily life is more and more characterized by ubiquitous interaction with computational devices and systems. For example, it is commonplace for a person walking a busy street, to be engaged in conversation with a distant person using telephony, while simultaneously receiving directions via a GPS-enabled web application on their mobile device. This overwhelming increase in human-computer interactions has prompted the need for a better understanding of how brain activity is shaped by performing sensorimotor actions in the physical world. In this context, neuroergonomics aims at bridging the gap between the abundant flow of information contained within a persons technological environment and related brain activity in order to adapt machine settings and facilitate optimal human-computer interactions (Parasuraman, 2013). One way to achieve this goal consists in developing adaptive systems. In neuroergonomics, adaptive automation relies on passive brain-computer interfaces (BCI) capable of spotting brain signatures linked to the operators cognitive state in order to adjust in real-time the operators technological environment. With the growing area of interest in this topic, the need for neuroimaging methods properly suited to ecological experimental settings has risen. In this vein, near-infrared spectroscopy (NIRS) presents some advantages as compared to other neuroimaging methods. In this opinion article, we first concentrate on the benefits of utilizing NIRS for investigation in neuroergonomics. Recent neuroergonomics investigations have used NIRS recordings in a number of laboratories (e.g., Ayaz et al., 2012; Mandrick et al., 2013a,b). It is particularly worth noting that most of these investigations have reported NIRS data from the prefrontal cortex (PFC). We provide a brief review of these recent studies and their impact in the field by presenting a detailed analysis of the applicability of NIRS-measured PFC activity to discriminate cognitive states in real life environments. In this paper, we will address two main questions: are NIRS-derived hemodynamic variables sufficiently sensitive to changes in sustained attention when measured over the PFC area? Are these measures useful for delineating different levels of mental workload?