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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. [ABSTRACT FROM AUTHOR]

One avenue of research for partial restoration of function following spinal cord injury is the use of neural prostheses, an example of which is functional electrical stimulation (FES) devices for motor functions. Neural prostheses may also be useful for the extraction of sensory information directly from the nervous system. We suggest the spinal cord as a possible site for the detection of peripheral sensory information from neural activity alone. Acute multichannel extracellular recordings were used to extract neural spike activity elicited from peripheral sensations from the spinal cords of rats. To test the recording method and classification potential, eight classes of sensory events were recorded consisting of electricai stimulation of seven locations on rat forepaws, and another class of data during which no stimulus was present. A dual-stage classification scheme using principal component analysis and k-Means clustering was devised to classify the sensory events during single trials. The eight tasks were correctly identified at a mean accuracy of 96%. Thus, we have shown the methodology to detect and classify peripheral sensory information from multichannel recordings of the spinal cord. These methods may be useful, for example, in a closed-loop FES for restoration of hand grasp. Index Terms--Brain interface, functional electrical stimulation, neural prosthesis, spinal cord injury.

A power spectral analysis study was conducted to investigate the effects of using an electromagnetic motion tracking sensor on an electroencephalogram (EEG) recording system. The results showed that the sensors do not generate any consistent frequency component(s) in the power spectrum of the EEG in the frequencies of interest (0.1-55 Hz). Index Terms--EEG, electroencephalography, motion tracking.

The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain-computer interface (BCI) technology for asynchronous control applications. The LF-ASD operates as an asynchronous brain switch (ABS) which is activated only when a user intends control and maintains an inactive state output when the user is not meaning to control the device (i.e., they may be idle, thinking about a problem, or performing some other action). Results from LF-ASD evaluations have shown promise, although the reported error rates are too high for most practical applications. This paper presents the evaluation of four new LF-ASD designs with data collected from individuals with high-level spinal cord injuries and able-bodied subjects. These new designs incorporated electroencephalographic energy normalization and feature space dimensionality reduction. The error characteristics of the new ABS designs were significantly better than the LF-ASD design with true positive rate increases of approximately 33% for false positive rates in the range of 1%-2%. The results demonstrate that the dimensionality of the LF-ASD feature space can be reduced without performance degradation. The results also confirm previous findings that spinal cord-injured subjects can operate ABS designs to the same ability as able-bodied subjects. Index Terms--Assistive technology, asynchronous control, brain-computer interface (BCI), intermittent control, neuroprosthesis, pattern recognition, statistical signal processing.

Studies of Birch's outlier processing method reveal that addition of time-varying influence functions to the algorithm enhances the detection and extraction of low SNRs of highly variable signals from white and colored noise processes. Use of a time-invariant minimum-mean-square-error filter does not yield results comparable to the new process. Simulations reveal the process efficiency at levels above -7.5 db.

A new approach to extracting single-trial event-related information is described in this paper. This approach, called the outlier processing method (OPM), is based on the concept that event-related information is contained in EEG time-series outliers. In particular, the OPM has been effective in extracting motor-related information from single-trial EEG. An investigation into the viability of the OPM was carried out on single-trial EEG data from four subjects. The EEG was collected under two conditions: an active task in which the subject performed a skilled thumb movement and an idle task in which the subject remained alert but did not carry out any motor activity. The results of this investigation demonstrated that consistent single-trial motor related information can be successfully extracted using the OPM.

Abstract Background A 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. Methods To 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. Results With 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%. Conclusions The 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.

Abstract Background Recently, 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. Methods In 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. Results An 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. Conclusion The 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.