EEG-Trockenelektroden und ihre Anwendungen bei BCI-Systemen

Heilinger, Alexander; Guger, Christoph

doi:10.1016/j.neulab.2019.05.003

Cited by 1 publication

(2 citation statements)

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“…Especially because of the advancement of neuro-brain interface technology, human-machine hybrid intelligence with a fusion of machine intelligence and bio-intelligence is considered as the ultimate goal of the future evolution of artificial intelligence. [3,4]. The human brain is an extremely complex system.…”

Section: Introductionmentioning

confidence: 99%

“…As a common artifact, ocular electricity is much larger than the amplitude of EEG, and the energy is mainly concentrated in the low-frequency band. It seriously affects the delta wave (0.5-3 HZ) and theta wave in the basic rhythm wave of EEG (4)(5)(6)(7). In order to eliminate the influence of ocular artifacts, in the clinic, doctors usually discard the EEG data segments containing ocular electrical interference, which may lead to the loss of some important EEG information.…”

Section: Introductionmentioning

confidence: 99%

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A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Fan

Cao

et al. 2019

Algorithms

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Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.

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Section: Introductionmentioning

confidence: 99%