A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

By Fabien Lotte, Laurent Bougrain, Andrzej S. Cichocki, Maureen Clerc, Marco Congedo, Alain Rakotomamonjy, Florian Yger. Journal of Neural Engineering, 2018

2,013 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/0c8ce4f11c00c78b04151d2526a812008ca7a764

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Citations in the corpus

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, background): “Although [31] believes that deep learning methods have not demonstrated convincing enhancement over some state-ofthe-art BCI methods, the results reviewed recently in [32] illustrated that some deep learning methods, for instance, convolutional neural networks (CNN), generative adversarial network (”
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, neutral): “Lotte et al. [46] provided a comprehensive review of classiﬁcation algorithms for EEG-based BCIs between 2007 and 2017.”
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data (2021, methodology): “The difficulty of learning both “lower-level” features and an expressive classifier simultaneously may help explain why work using DNNs to classify raw BCI data has tended to prefer shallower networks [9, 10, 2, 11, 12].”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “In addition, most existing machine learning research, if not all, centers on static data and, hence, is not able to classify rapidly changing brain signals accurately (Lotte et al., 2018).”
EEG Based Emotion Recognition: A Tutorial and Review (2022, background): “[23] mainly discusses the classiication algorithms for motor imagery-based BCI.”
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain–Machine Interfaces (2020, methodology): “ch bank common spatial pattern (FBCSP) [7] or Riemannian covariance [8] features in combination with robust classifiers like linear discriminant analysis (LDA) or support vector machines (SVMs) [6].”
Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “The design of classifiers for BCI comprises two issues [81], [82], [83].”
EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization (2022, background): “Advances in machine learning have facilitated the development of EEG classiﬁcation [25], [26], [27].”
Deep learning-based electroencephalography analysis: a systematic review (2019, background): “The performance of DLmethods on EEG is of great interest as it is still not clear whether DL can outperform traditional EEG processing pipelines [105].”
Mental State Classification Using Multi-Graph Features (2022, methodology): “In our experience, as seems to be the experience of others [16], current deep learning-based methods have not shown a signiﬁcant improvement over methods utilizing conventional features in multiple BCI applications [16] and are notably less interpretable.”