Brain-Computer Interface Use to Control Military Weapons and Tools

Czech, Adrian

doi:10.1007/978-3-030-72254-8_20

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…The highest value indicates the performance upper limit that the algorithm can achieve. For example, if the corresponding BCI system is applied in specific operational scenarios [ 36 , 37 ], selecting individuals with excellent performance from a large pool of operators can achieve the best system performance. The minimum value indicates the lower limit of algorithmic performance, which should be improved in mass usage scenarios [ 3 , 38 , 39 ] to enable a wider range of users to use it.…”

Section: Resultsmentioning

confidence: 99%

Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm

Liang

Zhang

Zhou

et al. 2023

Sensors

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Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems have been extensively researched over the past two decades, and multiple sets of standard datasets have been published and widely used. However, there are differences in sample distribution and collection equipment across different datasets, and there is a lack of a unified evaluation method. Most new SSVEP decoding algorithms are tested based on self-collected data or offline performance verification using one or two previous datasets, which can lead to performance differences when used in actual application scenarios. To address these issues, this paper proposed a SSVEP dataset evaluation method and analyzed six datasets with frequency and phase modulation paradigms to form an SSVEP algorithm evaluation dataset system. Finally, based on the above datasets, performance tests were carried out on the four existing SSVEP decoding algorithms. The findings reveal that the performance of the same algorithm varies significantly when tested on diverse datasets. Substantial performance variations were observed among subjects, ranging from the best-performing to the worst-performing. The above results demonstrate that the SSVEP dataset evaluation method can integrate six datasets to form a SSVEP algorithm performance testing dataset system. This system can test and verify the SSVEP decoding algorithm from different perspectives such as different subjects, different environments, and different equipment, which is helpful for the research of new SSVEP decoding algorithms and has significant reference value for other BCI application fields.

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Section: Resultsmentioning

confidence: 99%

Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm

Liang

Zhang

Zhou

et al. 2023

Sensors

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“…“replacement” is primarily for patients who have lost some function due to injury or disease. For example, people who have lost the ability to speak or speech can express themselves through a brain–computer interface (Ramakuri et al, 2019 ; Czech, 2021 ); groups of people with severe motor disabilities can communicate what they are thinking in their heads through a brain–computer interface system. “enhancement” refers to the strengthening of brain functions by implanting chips into the brain (Kotchetkov et al, 2010 ).…”

Section: Brain-like Computing Applicationmentioning

confidence: 99%

“…It has unique advantages in military application scenarios, especially in conditions with strong constraints on performance, speed, and power consumption. It can be used for ultra-low latency dynamic visual recognition against military targets in the sky, and the formation of a cognitive supercomputer to achieve rapid processing of massive amounts of data (Czech, 2021 ). In addition, brain-like computing can be used for intelligent gaming confrontation and decision-making in the future battlefield.…”

Section: Brain-like Computing Applicationmentioning

confidence: 99%

An overview of brain-like computing: Architecture, applications, and future trends

Xiao²,

Zhu³

et al. 2022

Front. Neurorobot.

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With the development of technology, Moore's law will come to an end, and scientists are trying to find a new way out in brain-like computing. But we still know very little about how the brain works. At the present stage of research, brain-like models are all structured to mimic the brain in order to achieve some of the brain's functions, and then continue to improve the theories and models. This article summarizes the important progress and status of brain-like computing, summarizes the generally accepted and feasible brain-like computing models, introduces, analyzes, and compares the more mature brain-like computing chips, outlines the attempts and challenges of brain-like computing applications at this stage, and looks forward to the future development of brain-like computing. It is hoped that the summarized results will help relevant researchers and practitioners to quickly grasp the research progress in the field of brain-like computing and acquire the application methods and related knowledge in this field.

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“…EEG has become the most widely used neuroimaging technique for brain-computer interfaces (BCI). Some of these extended uses of EEG include military operations such as controlling weapons or drones [4][5][6][7][8], educational classroom applications such as monitoring student's attention/other mental states or helping them engage with material [9][10][11][12][13], cognitive enhancement such as increasing cognitive load or focus [12,14,15], and consumer based games such as computer games or physical toys controlled via brain waves [2,[15][16][17][18][19].…”

Section: Introduction 1emotiv Epoc Wireless Eeg Device In Context: Cu...mentioning

confidence: 99%

Promise for Personalized Diagnosis? Assessing the Precision of Wireless Consumer-Grade Electroencephalography across Mental States

Lockman-Dufour

Buchanan

2022

Applied Sciences

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In the last decade there has been significant growth in the interest and application of using EEG (electroencephalography) outside of laboratory as well as in medical and clinical settings, for more ecological and mobile applications. However, for now such applications have mainly included military, educational, cognitive enhancement, and consumer-based games. Given the monetary and ecological advantages, consumer-grade EEG devices such as the Emotiv EPOC have emerged, however consumer-grade devices make certain compromises of data quality in order to become affordable and easy to use. The goal of this study was to investigate the reliability and accuracy of EPOC as compared to a research-grade device, Brainvision. To this end, we collected data from participants using both devices during three distinct cognitive tasks designed to elicit changes in arousal, valence, and cognitive load: namely, Affective Norms for English Words, International Affective Picture System, and the n-Back task. Our design and analytical strategies followed an ideographic person-level approach (electrode-wise analysis of vincentized repeated measures). We aimed to assess how well the Emotiv could differentiate between mental states using an Event-Related Band Power approach and EEG features such as amplitude and power, as compared to Brainvision. The Emotiv device was able to differentiate mental states during these tasks to some degree, however it was generally poorer than Brainvision, with smaller effect sizes. The Emotiv may be used with reasonable reliability and accuracy in ecological settings and in some clinical contexts (for example, for training professionals), however Brainvision or other, equivalent research-grade devices are still recommended for laboratory or medical based applications.

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Brain-Computer Interface Use to Control Military Weapons and Tools

Cited by 10 publications

References 27 publications

Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm

Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm

An overview of brain-like computing: Architecture, applications, and future trends

Promise for Personalized Diagnosis? Assessing the Precision of Wireless Consumer-Grade Electroencephalography across Mental States

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