Controlling In-Vehicle Systems with a Commercial EEG Headset: Performance and Cognitive Load

Cernea, Daniel; Olech, Peter-Scott; Ebert, Achim; Kerren, Andreas

doi:10.4230/oasics.vluds.2011.113

Cited by 7 publications

(1 citation statement)

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“…These devices range in price from low-cost options to more costly ones. While some of these devices cater more towards hobbyists rather than scientific or medical communities, their popularity and acceptable error margins [8] have led to their employment in numerous recent studies. The review of EEG headsets presented in this paper encompasses both the inexpensive and the expensive headsets, with a threshold of 1,000 USD separating them.…”

Section: Acquiring the Signalmentioning

confidence: 99%

Applications of Brain Computer Interface for Motor Imagery Using Deep Learning: Review on Recent Trends

Ahmed Zakaria Talha,

Noureldin S. Eissa,

Mohd Ibrahim Shapiai

2024

ARASET

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Motor Imagery-Brain Computer Interface (MI-BCI) is a very important technology gaining momentum throughout the last decade. This technology enables the linkage of brain activities to computer applications and can give disabled patients who suffer from motor disabilities (e.g., partial paralysis, muscle atrophy, etc.) the ability to interact normally with technologies around them. Currently, the technology is mostly limited to applications within dedicated laboratories and is hardly used in practical settings or in real-life applications. The purpose of this study is to review the latest trends and technologies in the field of MI-BCI, including the major challenges and the state-of-the-art classification techniques. The scope of this review article covers the feature selection algorithms that can help identify the most informative and discriminative features from the recorded brain signals, and the classification techniques that can identify the different types of motor movements.

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Section: Acquiring the Signalmentioning

confidence: 99%