Integration of cloud computing in BCI: A review

Kumar, Yogesh; Kumar, Jitender; Sheoran, Poonam

doi:10.1016/j.bspc.2023.105548

Cited by 6 publications

(2 citation statements)

References 92 publications

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“…In this work, we aimed to use less than 50% of the available channels. On the one hand, this addresses a big data processing issue for embedded BCI applications where computational resources are constrained; for instance, see [ 64 ]. On the other hand, selecting discriminating channels allowed us to minimize the influence of channels whose intrinsic characteristics did not significantly contribute to the classification process based on the information conveyed [ 65 ].…”

Section: Resultsmentioning

confidence: 99%

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures

Mwata-Velu,

Zamora,

Vasquez-Gomez

et al. 2024

Sensors

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This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain–computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).

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Section: Resultsmentioning

confidence: 99%

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures

Mwata-Velu,

Zamora,

Vasquez-Gomez

et al. 2024

Sensors

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“…Backup methods range from traditional devices to modern solutions (Browmik et al 2023). Loud services offer significant advantages, including elasticity, scalability, and availability, allowing regulatory institutions to store large volumes of data at a reasonable cost and ensure their integration (Kumar et al 2024). Backups must be created regularly and automatically to minimize the risk of losing up-to-date data.…”

Section: Information Management Methodsmentioning

confidence: 99%

Investigating the Application of Digital Tools for Information Management in Financial Control: Evidence from Bulgaria

Zhelev,

Kostova

2024

JRFM

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This paper discusses the application of digital information management tools in the context of financial control. In Bulgaria, such research is innovative as it is the first time that digital transformation in crucial financial control institutions, which influence the formation of the revenue part of the state budget and the spending of public funds, has been studied. The study aims to answer the research question of to what extent the application of digital tools in financial control improves its effectiveness. It analyses how modern technologies improve the efficiency and accuracy of information used in financial control institutions. The authors examine the impact of digital tools, such as database management systems, business analytics platforms, and electronic document management tools, on collecting, analyzing, and managing financial and non-financial information. The study uses descriptive statistics and a correlation analysis, which significantly contributes to establishing the relationship between implemented digital tools and improvements in financial control procedures. The results show that despite the conditions created for digitalization in financial control institutions, digital tools are used to a limited extent in the information management process. The study emphasizes the need for continuous investment in digital technologies and training to maximize the benefits of their application in financial control.

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eXtended Reality Services for Outdoor Cultural Spaces: System Design and Evaluation Framework Using Unbiased Biometric Data

Koukopoulos,

Fidas

2024

2024 15th International Conference on Information, Intelligence, Systems &Amp;amp; Applications (IISA)

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Integration of cloud computing in BCI: A review

Cited by 6 publications

References 92 publications

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures

Investigating the Application of Digital Tools for Information Management in Financial Control: Evidence from Bulgaria

eXtended Reality Services for Outdoor Cultural Spaces: System Design and Evaluation Framework Using Unbiased Biometric Data

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