Neil Squire Society

Abstract The partnered research method, used routinely in other fields, offers great potential to improve the quality and practical use of applied psychology research. Partnered research integrates the perspectives of researchers, knowledge users, people who have lived experience with the attributes being studied, and other stakeholders in all elements of the research process, from the creation and generation of research questions to the methods used, the data analyzed, and the dissemination, application, and implementation of research results. We explain the concept of partnered research and provide a step‐by‐step roadmap for applied psychology scholars interested in conducting partnered research. In doing so, we also address common challenges with this method and provide advice on how to overcome them. We embed our description of the partnered research approach primarily in the context of research on disabilities and work but also offer examples drawn from other areas of applied psychology.

BackgroundA novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard. To select a letter, the user must gaze at the target for at least a specific period of time (dwell time) and then activate the BCI by performing a mental task. Unfortunately, electroencephalogram (EEG) signals are often contaminated with artefacts. Artefacts change the quality of EEG signals and subsequently degrade the BCI’s performance.MethodsTo remove artefacts in EEG signals, the proposed algorithm uses the stationary wavelet transform combined with a new adaptive thresholding mechanism. To evaluate the performance of the proposed algorithm and other artefact handling/removal methods, semi-simulated EEG signals (i.e., real EEG signals mixed with simulated artefacts) and real EEG signals obtained from seven participants are used. For real EEG signals, the hybrid BCI system’s performance is evaluated in an online-like manner, i.e., using the continuous data from the last session as in a real-time environment.ResultsWith semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains. With real EEG signals, we demonstrate that for dwell time of 0.0s, the number of false-positives/minute is 2 and the true positive rate (TPR) achieved by the proposed algorithm is 44.7%, which is more than 15.0% higher compared to other state-of-the-art artefact handling methods. As dwell time increases to 1.0s, the TPR increases to 73.1%.ConclusionsThe proposed artefact removal algorithm greatly improves the BCI’s performance. It also has the following advantages: a) it does not require additional electrooculogram/electromyogram channels, long data segments or a large number of EEG channels, b) it allows real-time processing, and c) it reduces signal distortion.

Information and communication technologies (ICT) are directly or indirectly a form of assistive technology. Indeed, many forms of new emerging ICT have the potential to provide opportunities for persons with disabilities to obtain inclusion in the mainstream of society to an extent never before accomplished. However, due to the rapid ubiquitous insertion of these technologies into our society and given that most of these technologies are not accessible to persons with disabilities they are forming new barriers rather than opening up new opportunities. New strategies need to be adopted to deal with this trend and, particularly in the relatively near term, it is argued that the most important strategy is the development of enforceable regulations and standards that impact the accessibility of these new emerging technologies.

While the inherently small market for assistive technology (AT) can be a significant hurdle in the development of AT solutions, small markets can also provide opportunities to foster technology innovation under the right circumstances. Focusing on the wheelchair industry, I summarize the numerous obstacles that small companies face in trying to address small AT markets, as well as the range of opportunities available to assist small companies in their efforts to impact the quality of life of those with disabilities. Indeed, small market AT may be a perfect fit for the growing field of “small batch” manufacturing in combination with motivated individuals suddenly empowered by a host of new technologies. I close with a brief discussion of future possibilities for AT in small markets.

BackgroundRecently, successful applications of the discrete wavelet transform have been reported in brain interface (BI) systems with one or two EEG channels. For a multi-channel BI system, however, the high dimensionality of the generated wavelet features space poses a challenging problem.MethodsIn this paper, a feature selection method that effectively reduces the dimensionality of the feature space of a multi-channel, self-paced BI system is proposed. The proposed method uses a two-stage feature selection scheme to select the most suitable movement-related potential features from the feature space. The first stage employs mutual information to filter out the least discriminant features, resulting in a reduced feature space. Then a genetic algorithm is applied to the reduced feature space to further reduce its dimensionality and select the best set of features.ResultsAn offline analysis of the EEG signals (18 bipolar EEG channels) of four able-bodied subjects showed that the proposed method acquires low false positive rates at a reasonably high true positive rate. The results also show that features selected from different channels varied considerably from one subject to another.ConclusionThe proposed hybrid method effectively reduces the high dimensionality of the feature space. The variability in features among subjects indicates that a user-customized BI system needs to be developed for individual users.

When we recognize the development and use of information technologies as an emerging global public good, we can move away from the stark dualisms of profit versus human rights, market share versus accessibility, and competition versus inclusion, to an understanding of how both public and private interests are at play in the development of this global public good. Governments, industry, and disability advocacy organizations are all involved in the construction of this public good in a complex three-way dance. We argue that in the context of this three-way dance it is possible to create a common ground between disability advocacy organizations and the IT industry by developing tools that address the tensions that arise from their differing motivating forces. Specifically, we argue that three sets of tools—regulation, developing ease of use products and standards, and using education to increase the market strength of people with disabilities as consumers—can change the relationships between these two communities of interest in ways that will benefit each.

This article examines challenges of engaging the business/industry sector in research on the use of information technology to enhance accessibility for people with disabilities in two areas of common interest—to employment and to public and retail services. The data presented arise from the joint effort of two research teams who independently encountered challenges in engaging private sector firms in their respective projects. Using a case study approach, the experiences of both groups were examined for themes representing factors that inhibited collaboration between research and business sectors, and those that enhanced collaboration. Trustworthiness of themes was established by submitting them for critique and feedback to key informants knowledgeable in both business and research. From a social systems theory perspective, findings suggest that the most important differences between research and business systems reside in the meaning of communication each uses, and differences in assumptions about and value placed on factors such as pursuit of new knowledge, the importance of marketability of findings, and so on. Additional complications arise in pursuit of research related to disability. Factors to build on when seeking research collaboration include an understanding of the language and culture of business systems, and the very real possibility of developing disability research into secondary goals that business systems typically pursue once the prime need for profitability has been at addressed (the notion of satisficing). Implications for communication between disability research and business systems are identified.

Most existing brain-computer interfaces (BCIs) detect specific mental activity in a so-called synchronous paradigm. Unlike synchronous systems which are operational at specific system-defined periods, self-paced (asynchronous) interfaces have the advantage of being operational at all times. The low-frequency asynchronous switch design (LF-ASD) is a 2-state self-paced BCI that detects the presence of a specific finger movement in the ongoing EEG. Recent evaluations of the 2-state LF-ASD show an average true positive rate of 41% at the fixed false positive rate of 1%. This paper proposes two designs for a 3-state self-paced BCI that is capable of handling idle brain state. The two proposed designs aim at detecting right- and left-hand extensions from the ongoing EEG. They are formed of two consecutive detectors. The first detects the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement). It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1%) in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1%) in the context of a 2-state self-paced BCI.

Information transfer rate (ITR) is used as the primary metric in the evaluation of many synchronized brain interface (BI) systems. In this paper, the ITR of self-paced BI systems is formulated. It is shown that ITR by itself is NOT a suitable performance measure for self-paced evaluation. Only when there are additional conditions, ITR can be used as a suitable performance measure. Since ITR is achieved through the analysis of the outputs of BI systems, under specific conditions it can be used for comparison between different self-paced BI systems which use similar protocols