Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces

By Fabien Lotte. Proceedings of the IEEE, 2015

303 citations recorded by the source corpus.

How researchers see this paper

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

Riemannian Approaches in Brain-Computer Interfaces: A Review (2017, methodology): “so, such as subject-to-subject transfer, semi-supervised learning, or artificial data generation [13].”
EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization (2022, background): “Follow [37], the training samples of the same category are equally divided into N s segments, then randomly concatenated while maintaining the original time order.”
Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks (2019, methodology): “Over the course of numerous BCI studies [16]–[21], growing attention has been dedicated to the analysis of electroencephalography (EEG) signals, especially by movement imagination, called motor imagery (MI) [1], [2], [8], [12], [14], [16], [22].”
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, background): “robustness of the LDA to mislabelling [137].”