Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment

By Robert Leeb, Felix Lee, Claudia Keinrath, Reinhold Scherer, Horst Bischof, Gert Pfurtscheller. IEEE transactions on neural systems and rehabilitation engineering, 2007

528 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/49ae122ebee3c9e894ba4d7a71f462b7344a287f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Brain-Computer Interfaces, Virtual Reality, and Videogames (2008, methodology): “MI-based BCIs have also been used to control the steering of a virtual car [2], explore a virtual bar [10], or move along a virtual street [3] or through a virtual flat [7].”
A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines (2017, methodology): “To verify the performance of the proposed scheme for motor imagery classification, extensive and systematic experiments have been conducted on BCI competition IV data set 2b [38].”
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, methodology): “Each subject participated in two sessions performed on two different Each session contained six runs with ten trials each and two MI classes [118]. days.”
Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI (2011, background): “In some studies [20]–[22], a minimum number of channels (namely C3, C4, and Cz) are deployed for motor imagery-based BCI.”
Toward a hybrid brain–computer interface based on imagined movement and visual attention (2010, methodology): “The SFFS/LDA procedure is well established in the BCI literature in papers from our group [20, 30, 31] and other groups [32, 33].”
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification (2018, background): “The classiﬁcation of brain signals has found extensive applications in brain-computer interface, entertainment and rehabilitation engineering [37].”
Review of the BCI Competition IV (2012, methodology): “This data set consists of EEG data from 9 subjects of a study published in Leeb et al. (2007).”
Could Anyone Use a BCI? (2010, background): “Some approaches rely on specific imagined movements, such as moving the left hand, right hand, or both feet (Pfurtscheller et al., 2006; Leeb et al., 2007; Blankertz et al., 2008; Scherer et al., 2004, 2008).”
A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces (2009, methodology): “The proposed method enhances the classification accuracy of BCI competition III dataset IVa and BCI competition IV dataset IIb.”
A novel deep learning approach for classification of EEG motor imagery signals (2017, methodology): “To evaluate our method we used dataset III from BCI Competition II [29] and training set of dataset 2b from BCI Competition IV [31].”
Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “In these studies, BCI users who use immersive Virtual Environments (VEs) make fewer errors, report that BCIs are easier to learn and use, and state that they enjoy BCI use more [99, 98, 159].”
BCI Competition 2008 { Graz data set B (2008, methodology): “This data set consists of EEG data from 9 subjects of a study published in [ 1 ].”