Demonstration of low-cost EEG system providing on-demand communication for locked-in patients

Koo, Dongho; Polanco, Hernan F. Herrera; Cross, Michael; Rho, Yeon Hee; Ames, Nicholas; Raiti, John

doi:10.1109/ghtc53159.2021.9612454

2021 IEEE Global Humanitarian Technology Conference (GHTC) 2021

DOI: 10.1109/ghtc53159.2021.9612454

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Demonstration of low-cost EEG system providing on-demand communication for locked-in patients

Dongho Koo

Hernan F. Herrera Polanco

Michael Cross

et al.

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“…Notice that the works citing Peterson et al paper have exploited it to make a point regarding the necessity of moving to low-cost and consumer-grade technologies to provide more ecological and easy-to-use BCIs. For example, Koo et al[16] cite it when considering that locked-in patients do not have communication tools based on BCI paradigms that are easy to maintain, low cost and comfortable. Moreover, Peterson et al work is taken as an example considering the use of open-source and low cost platforms[2] [40] like the OpenBCI one.…”

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The evolution of AI approaches for motor imagery EEG-based BCIs

Saibene¹,

Corchs²,

Caglioni³

et al. 2022

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The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to motor imagination. Therefore, these systems open the possibility of developing applications that could span from the medical field to the entertainment industry. In this context, Artificial Intelligence (AI) approaches become of fundamental importance especially when wanting to provide a correct and coherent feedback to BCI users. Moreover, publicly available datasets in the field of MI EEG-based BCIs have been widely exploited to test new techniques from the AI domain. In this work, AI approaches applied to datasets collected in different years and with different devices but with coherent experimental paradigms are investigated with the aim of providing a concise yet sufficiently comprehensive survey on the evolution and influence of AI techniques on MI EEG-based BCI data.

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mentioning

confidence: 99%

The evolution of AI approaches for motor imagery EEG-based BCIs

Saibene¹,

Corchs²,

Caglioni³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

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Demonstration of low-cost EEG system providing on-demand communication for locked-in patients

Cited by 1 publication

References 10 publications

The evolution of AI approaches for motor imagery EEG-based BCIs

The evolution of AI approaches for motor imagery EEG-based BCIs

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