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Chapter
The (Un)reliability of Saliency Methods
Saliency methods aim to explain the predictions of deep neural networks. These methods lack reliability when the explanation is sensitive to factors that do not contribute to the model prediction. We use a sim...
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Chapter and Conference Paper
Prediction of Difficulty Levels in Video Games from Ongoing EEG
Real-time assessment of mental workload from EEG plays an important role in enhancing symbiotic interaction of human operators in immersive environments. In this study we thus aimed at predicting the difficult...
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Article
Open AccessDecoding of top-down cognitive processing for SSVEP-controlled BMI
We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI th...
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Article
Open AccessWyrm: A Brain-Computer Interface Toolbox in Python
In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machin...
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Chapter and Conference Paper
Slow Feature Analysis - A Tool for Extraction of Discriminating Event-Related Potentials in Brain-Computer Interfaces
The unsupervised signal decomposition method Slow Feature Analysis (SFA) is applied as a preprocessing tool in the context of EEG based Brain-Computer Interfaces (BCI). Classification results based on a SFA de...
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Article
Open AccessLearning complex cell units from simulated prenatal retinal waves with slow feature analysis
