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Gary Garcia-Molina
Researcher at Philips
Publications - 36
Citations - 713
Gary Garcia-Molina is an academic researcher from Philips. The author has contributed to research in topics: Non-rapid eye movement sleep & Sleep Stages. The author has an hindex of 11, co-authored 30 publications receiving 566 citations. Previous affiliations of Gary Garcia-Molina include University of Wisconsin-Madison.
Papers
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Journal ArticleDOI
Enhancement of sleep slow waves: underlying mechanisms and practical consequences.
Michele Bellesi,Brady A. Riedner,Gary Garcia-Molina,Gary Garcia-Molina,Chiara Cirelli,Giulio Tononi +5 more
TL;DR: The converging evidence showing that acoustic stimulation is safe and represents an ideal tool for slow wave sleep (SWS) enhancement is reviewed, highlighting the physiology of the K-complex, a peripheral evoked slow wave, and how intensity and frequency of the acoustic stimuli affect sleep enhancement.
Journal ArticleDOI
Emotional brain-computer interfaces
TL;DR: Control of a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates and the ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation.
Proceedings ArticleDOI
Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal.
TL;DR: It is determined that frontal EEG signals, with spectral linear features, epoch durations between 18 and 30 seconds, and a random forest classifier lead to optimal classification performance while ensuring real-time online operation.
Proceedings ArticleDOI
Optimal spatial filtering for the steady state visual evoked potential: BCI application
Gary Garcia-Molina,Danhua Zhu +1 more
TL;DR: This paper proposes a taxonomy to categorize these methods and extensively evaluate them using 22 stimulation frequencies and suggests improvements to existing methods to increase the SSVEP detection performance.
Journal ArticleDOI
Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG.
TL;DR: This work gives the first detailed account of CONV/LSTM network design process for EEG sleep staging in single channel home based setting and achieves a performance close to the previously reported human inter-expert agreement of Kappa 0.75.