BCILAB: a platform for brain–computer interface development

By Christian Andreas Kothe, Scott Makeig. Journal of Neural Engineering, 2013

384 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/224d27d353b6c0d6bf2fd8990fb1bf7c23ebb92b

How researchers see this paper

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

In Cognitive Neuroscience

1. Luca Pion-Tonachini
2. Tzyy-Ping Jung
3. C. Wilkinson
4. Chi-Yuan Chang
5. Sheng-Hsiou Hsu
6. Scott Makeig
7. Adriana S Méndez Leal
8. A. Levin

In Radiology, Nuclear Medicine and Imaging

1. Tzyy-Ping Jung
2. I. Chavdarov
3. A. Lekova
4. Luca Pion-Tonachini
5. Chi-Yuan Chang
6. Sheng-Hsiou Hsu
7. T. Mullen
8. A. Levin

In Human-Computer Interaction

1. I. Chavdarov
2. A. Lekova
3. Tzyy-Ping Jung
4. T. Mullen
5. Luca Pion-Tonachini
6. Chi-Yuan Chang
7. A. Levin
8. L. J. Gabard-Durnam

Citations in the corpus

• Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, positive): “We have made the workflow available as the BCILAB "Source Information Flow Toolbox Adapter" paradigm (ParadigmSIFT class).”
• Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces (2018, background): “2742541 the user through the analysis of neurophysiological signals [24], [32].”
• The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, methodology): “The ASR approach was then carried out with EEGlab’s Clean_rawdata functions adopted from BCILAB (Kothe and Makeig, 2013; Mullen et al., 2015).”
• The PREP pipeline: standardized preprocessing for large-scale EEG analysis (2015, methodology): “The implementation is based on functions in BCILAB (Kothe and Makeig, 2013).”
• Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings (2020, methodology): “From our empirical observation, ASR could achieve effective artifact removal for standard 20-channel EEG. ASR can also be a powerful tool for online, near-real-time, automatic artifact removal, which has been implemented and disseminated in BCILAB [44] and in the Real-time EEG Sourcemapping Toolbox ”
• A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations (2021, methodology): “BCILAB [2], an open source MATLAB-based toolbox, provides an organized collection of 100 preimplemented methods and method variants.”