Brain-Computer Interfaces: Beyond Medical Applications

By J. V. Erp, Fabien Lotte, Michael W. Tangermann. Computer, 2012

376 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/15cd5d641accc81dcd64469526127706954dff26

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2018, background): “Recently, the application scope of BCIs has been extended to able-bodied people [23], [34], and EEG becomes the most popular input signal because it is easy and safe to acquire, and has high temporal resolution.”
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, neutral): “BCIs were initially proposed for disabled people [6], but their current application scope has been extended to able-bodied users [7], in gaming [8], emotion recognition [9], mental fatigue evaluation [10], vigilance estimation [11], [12], etc.”
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, background): “Among the different reasons currently preventing BCI from being widely used outside laboratories for practical applications, one can cite their lack of robustness and reliability, their cum-bersome setup and their long calibration time [1], [8], [9].”
Brain computer interfacing: Applications and challenges (2015, background): “It does not require any external devices or muscle intervention to issue commands and complete the interaction [1].”
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, background): “text input systems or wheelchairs [181], as rehabilitation devices for stroke patients [8], as new gaming input devices [52], or to design adaptive human–computer interfaces that can react to the user’s mental states [237], to name a few [45, 216].”
Riemannian Approaches in Brain-Computer Interfaces: A Review (2017, neutral): “, to provide communication to severely paralyzed users [3], as a new control device for gaming [4] or to design adaptive human–computer interfaces that can react to the user’s mental state [5], to name a few [6].”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, background): “Through this system, from the issuance of the commands to the completion of the interaction, no external devices or muscle intervention is required (Van Erp et al., 2012).”