A Graph-Based Feature Extraction Algorithm Towards a Robust Data Fusion Framework for Brain-Computer Interfaces

Zhu, Shaotong; Hosni, Sarah Ismail; Huang, Xiaofei; Borgheai, Seyyed Bahram; McLinden, John; Shahriari, Yalda; Ostadabbas, Sarah

doi:10.1109/embc46164.2021.9630804

Cited by 4 publications

(1 citation statement)

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“…fNIRS can be portable, easier to implement, and less susceptible to artifacts compared to EEG [26], but it can be more expensive. The two techniques can be coupled together for enhanced performance, such as in the work of [27], [28]. In addition, medical imaging techniques can be applied with EEG and fNIRS, but those are rarely used for engineering applications due to their complexity and high cost.…”

Section: B Neural Activity Measuring Techniquesmentioning

confidence: 99%

Towards Practical BCI-Driven Wheelchairs: A Systematic Review Study

Naser

Bhattacharya

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The use of brain signals in controlling wheelchairs is a promising solution for many disabled individuals, specifically those who are suffering from motor neuron disease affecting the proper functioning of their motor units. Almost two decades since the first work, the applicability of EEG-driven wheelchairs is still limited to laboratory environments. In this work, a systematic review study has been conducted to identify the state-of-the-art and the different models adopted in the literature. Furthermore, a strong emphasis is devoted to introducing the challenges impeding a broad use of the technology as well as the latest research trends in each of those areas.

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Section: B Neural Activity Measuring Techniquesmentioning

confidence: 99%