Identifying risk controls for future advanced brain-computer interfaces: A prospective risk assessment approach using work domain analysis

King, Brandon; Read, Gemma J. M.; Salmon, Paul M.

doi:10.1016/j.apergo.2023.104028

Cited by 9 publications

(2 citation statements)

References 46 publications

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“…In the future, we will also consider exploring novel methods to address the nonstationarity of the EEG signal so as to further improve the robustness of the online BCI system. Moreover, there are some risks in the application of BCI systems, which also needs to be further considered in the future research (King et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Characterization and classification of EEG signals evoked by different CAD models

Niu,

Hao,

Ming

et al. 2024

Hum Ftrs & Erg Mfg Svc

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The past two decades have witnessed dramatic advancement in computer‐aided design (CAD). However, development of human–computer interfaces (HCI) for CAD have not kept up with these advances. Windows, Icons, Menus, Pointer (WIMP) is still the mainly used interface for CAD applications which limits the naturalness and intuitiveness of the CAD modeling process. As a novel interface, Brain–computer interfaces (BCIs) have great potential in the application of CAD modeling. Utilizing BCIs, the user can create CAD models just by thinking about it in principle, because BCIs provide an end‐to‐end interaction channel between users and CAD models. However, current related studies are mainly limited to the existing BCIs paradigms, while ignoring the relationship between electroencephalogram (EEG) signals and CAD models, which largely increases the cognitive load on the users. In this study, we aimed to explore the potential of using BCI to create CAD models directly independent of the classical BCIs paradigms. For this purpose, EEG signals evoked by six basic CAD models (i.e., point, square, trapezoid, line, triangle, and circle) were collected from 28 participants. After preprocessing and sub‐trial principal components analysis (st‐PCA) of recorded data, the peak, mean and time‐frequency energy features were extracted from EEG signals. By applying the one‐way repeated measures analysis of variance, we demonstrated that there were significant differences among these EEG features evoked by different CAD models. These features from EEG electrode channels ranked by mutual information were then used to train a discriminant classifier of genetic algorithm‐based support vector machine. The empirical result showed that this classifier can discriminate the CAD models with an average accuracy of about 72%, which turns out that EEG based model generation is feasible, and provides the technical and theoretical basis for building a novel BCI for CAD modeling.

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Section: Discussionmentioning

confidence: 99%

Characterization and classification of EEG signals evoked by different CAD models

Niu,

Hao,

Ming

et al. 2024

Hum Ftrs & Erg Mfg Svc

View full text Add to dashboard Cite

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“…The updated AH model was then sent for a final review by the participants over a two-week period. This final feedback was reviewed and used to finalize the model, as seen in King et al (2023).…”

Section: Abstraction Hierarchy Development and Validationmentioning

confidence: 99%

Optimizing Future Advanced BCIs: Using WDA to Identify Potential Pain Points in BCI Systems

King,

Read,

Salmon

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Brain-computer interfaces (BCIs) are a rapidly developing technology with the potential for a wide range of applications, yet the technology and its possible risks are poorly understood. The aim of this study was to develop and analyse an abstraction hierarchy model of a hypothetical, advanced invasive BCI system lifecycle to identify potential design lifecycle risks. Ten subject matter experts participated in a workshop to validate a BCI lifecycle abstraction hierarchy, which was subsequently analysed using the Sociotechnical Systems-Design Toolkit (STS-DT) prompts. The analysis identified the system purpose of generating wealth for companies and shareholders conflicted with other system purposes relating to health and wellbeing. Findings also identified poorly supported system functions, such as the regulation of BCI technologies, and potentially unreliable system objects. In conclusion, it is recommended that the identified issues be addressed through a sociotechnical systems approach focusing on joint optimisation across the system.

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Towards Holistic Functional Task Analysis

Salehi,

Salmon,

Burns

et al. 2024

Hum Ftrs & Erg Mfg Svc

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Task analysis (TA) can contribute to work systems design, accident investigation, risk assessment, human error identification, planning, and training. Despite the advantages of existing sequential and hierarchical methods, they decompose tasks into their structure and focus on the order in which tasks are accomplished. They do not trace all interactions among elements/subtasks/operations at different levels. As the complexity of tasks increases, not keeping track of all interactions may result in poor, unwanted outcomes. This research introduces a different approach to TA that decomposes tasks into their constituent functions, describes the functionality of the overall work system, traces (dynamic nonlinear) interactions among functions, and highlights the role of functional variability in forming emergent outcomes. This approach to TA is called functional task analysis (FTA). A case study on nursing work was used to demonstrate the suitability of the FTA approach. The findings of this study show that the FTA approach contributes to task modeling by building a nonsequential, nonhierarchical functional model of a complex task considering dynamic, nonlinear interactions among functions. The FTA also contributes to task description by explaining different ways a task can be accomplished. It also increases the understanding, interpretation, and analysis of how changes in work conditions shape good/acceptable and poor/unacceptable outcomes. The FTA can complement the TA by adding some aspects, including functionality, nonlinearity, dynamics, and emergence, that the TA does not normally consider. The findings highlight how the functional approach to TA can be deployed as an alternative (or complement) to other task analysis methods.

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Identifying risk controls for future advanced brain-computer interfaces: A prospective risk assessment approach using work domain analysis

Cited by 9 publications

References 46 publications

Characterization and classification of EEG signals evoked by different CAD models

Characterization and classification of EEG signals evoked by different CAD models

Optimizing Future Advanced BCIs: Using WDA to Identify Potential Pain Points in BCI Systems

Towards Holistic Functional Task Analysis

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