Recent advances and current trends in brain‐computer interface research and their applications

Žabčíková, Martina; Koudelková, Zuzana; Jašek, Roman; Lorenzo-Navarro, Javier

doi:10.1002/jdn.10166

Cited by 29 publications

(16 citation statements)

References 113 publications

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“…However, it has been recently observed that, with advances in sensor-based technologies, this is no longer as impactful a limitation as once it was. For example, the field of passive brain-computer interfaces (passive-BCI) is focused on assessing and interpreting changes in the user state during Human-Computer Interaction (Zander et al, 2010 ; Zander and Kothe, 2011 ; Zabcikova et al, 2022 ). In particular, in the context of neurophysiological measurement, passive-BCI algorithms and biosignal acquisition procedures have allowed the identification and quantification of relevant mental and emotional states of humans.…”

Section: Measuring Mental Workloadmentioning

confidence: 99%

Human Mental Workload: A Survey and a Novel Inclusive Definition

et al. 2022

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Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.

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Section: Measuring Mental Workloadmentioning

confidence: 99%

Human Mental Workload: A Survey and a Novel Inclusive Definition

et al. 2022

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“…In recent years, the Brain-Computer Interface application has been growing rapidly, establishing itself as an emerging technology [ 87 ] tested in several scenarios such as rehabilitation [ 88 ], robotics [ 89 ], precision surgery and speech recognition. However, the usability of many professional brain-sensing equipment remains limited.…”

Section: Ms Hololens 2 Applications In Medical and Healthcare Context...mentioning

confidence: 99%

Microsoft HoloLens 2 in Medical and Healthcare Context: State of the Art and Future Prospects

Palumbo

2022

Sensors

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In the world reference context, although virtual reality, augmented reality and mixed reality have been emerging methodologies for several years, only today technological and scientific advances have made them suitable to revolutionize clinical care and medical contexts through the provision of enhanced functionalities and improved health services. This systematic review provides the state-of-the-art applications of the Microsoft® HoloLens 2 in a medical and healthcare context. Focusing on the potential that this technology has in providing digitally supported clinical care, also but not only in relation to the COVID-19 pandemic, studies that proved the applicability and feasibility of HoloLens 2 in a medical and healthcare scenario were considered. The review presents a thorough examination of the different studies conducted since 2019, focusing on HoloLens 2 medical sub-field applications, device functionalities provided to users, software/platform/framework used, as well as the study validation. The results provided in this paper could highlight the potential and limitations of the HoloLens 2-based innovative solutions and bring focus to emerging research topics, such as telemedicine, remote control and motor rehabilitation.

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“…Electroencephalogram (EEG) reflects the spontaneous and rhythmic potential changes generated by neurons in the cerebral cortex, which is widely used in emotion recognition [ 1 – 3 ] and Parkinson’s disease detection [ 4 , 5 ], especially in brain-computer interfaces (BCI) [ 6 – 8 ]. Motor imagery EEG (MI-EEG) based BCI can improve the quality of life for patients with neurological disabilities by translating brain activity directly into command signals through electronic devices.…”

Section: Introductionmentioning

confidence: 99%

A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification

2022

Appl Intell

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Domain adaptation, as an important branch of transfer learning, can be applied to cope with data insufficiency and high subject variabilities in motor imagery electroencephalogram (MI-EEG) based brain-computer interfaces. The existing methods generally focus on aligning data and feature distribution; however, aligning each source domain with the informative samples of the target domain and seeking the most appropriate source domains to enhance the classification effect has not been considered. In this paper, we propose a dual alignment-based multi-source domain adaptation framework, denoted DAMSDAF. Based on continuous wavelet transform, all channels of MI-EEG signals are converted respectively and the generated time-frequency spectrum images are stitched to construct multi-source domains and target domain. Then, the informative samples close to the decision boundary are found in the target domain by using entropy, and they are employed to align and reassign each source domain with normalized mutual information. Furthermore, a multi-branch deep network (MBDN) is designed, and the maximum mean discrepancy is embedded in each branch to realign the specific feature distribution. Each branch is separately trained by an aligned source domain, and all the single branch transfer accuracies are arranged in descending order and utilized for weighted prediction of MBDN. Therefore, the most suitable number of source domains with top weights can be automatically determined. Extensive experiments are conducted based on 3 public MI-EEG datasets. DAMSDAF achieves the classification accuracies of 92.56%, 69.45% and 89.57%, and the statistical analysis is performed by the kappa value and t- test. Experimental results show that DAMSDAF significantly improves the transfer effects compared to the present methods, indicating that dual alignment can sufficiently use the different weighted samples and even source domains at different levels as well as realizing optimal selection of multi-source domains.

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Recent advances and current trends in brain‐computer interface research and their applications

Cited by 29 publications

References 113 publications

Human Mental Workload: A Survey and a Novel Inclusive Definition

Human Mental Workload: A Survey and a Novel Inclusive Definition

Microsoft HoloLens 2 in Medical and Healthcare Context: State of the Art and Future Prospects

A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification

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