A review of classification algorithms for EEG-based brain–computer interfaces
Explore this paper's citation graph
Summary
This paper compares classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG) in terms of performance and provides guidelines to choose the suitable classification algorithm(s) for a specific BCI.
- Type
- review
- Published
- 2007-01-31
- Cited by
- 2,749
- References
- 93
- Access
- Open access
- OpenAlex
- https://openalex.org/W2075647286
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16362395
Keywords
Brain–computer interface, Electroencephalography, Computer science, Interface (matter), Algorithm
References
- The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials
- Stacked generalization
- Classification of EEG Signals from Four Subjects During Five Mental Tasks
- CLASSIFICATION OF SINGLE TRIAL EEG SIGNALS BY A COMBINED PRINCIPAL + INDEPENDENT COMPONENT ANALYSIS AND PROBABILISTIC NEURAL NETWORK APPROACH
- Discriminative vs Informative Learning
- The Use of Fuzzy Inference Systems for Classification in EEG-based Brain-Computer Interfaces
- HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems
- Fuzzy models for pattern recognition
- A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces
- On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
- Classification of spatio-temporal EEG readiness potentials towards the development of a brain-computer interface
- EEG-based communication via dynamic neural network models
- Classification of movement intention by spatially filtered electromagnetic inverse solutions
- Support vector machines: hype or hallelujah?
- Brain-computer communication: unlocking the locked in.
- Different classification techniques considering brain computer interface applications
- The self-organizing map
- PCA+HMM+SVM for EEG pattern classification
- Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
- EEG topography recognition by neural networks
Cited by
- ERP classification using empirical mode decomposition
- Clasificación de características de electroencefalogramas en sistemas Brain Computer Interface basados en ritmos sensoriomotores
- Time-frequency selection in two bipolar channels for improving the classification of motor imagery EEG
- Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals
- Modality-specific spectral dynamics in response to visual and tactile sequential shape information processing tasks: An MEG study using multivariate pattern classification analysis.
- Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks
- An Evaluation of Training with an Auditory P300 Brain-Computer Interface for the Japanese Hiragana Syllabary
- Detecting the no-control state in self-paced Brain-Computer Interfaces
- Robust, Automated Methods for Filtering and Processing Neural Signals
- Electroencéphalographie et Interfaces Cerveau-Machine : nouvelles méthodes pour étudier les états mentaux
- Brain-computer interfaces and neurorehabilitation.
- An Efficient P300-based Brain-Computer Interface with Minimal Calibration Time
- Automated sleep classification using the new sleep stage standards
- Classification of EEG Signals in a Brain-Computer Interface System
- Interfaces Cerveau-Machines basées sur l'imagination de mouvements brefs : vers des boutons contrôlés par la pensée. (Brain-Machine Interfaces based on brief imaginary movements: towards brain-controlled buttons)
- A Critical Review on the Usage of Ensembles for BCI
- Etude sur la pensée animale: continuité neuro-cognitive de la catégorisation visuelle
- An HMM-PCA approach for EEG-based brain computer interfaces (BCIs)
- Examination and comparison of methods to increase communication speed of paralysed patients by brain-computer interfaces
- Brain-Computer Interface In Control Systems
Related papers
- A Novel BCI Paradigm Combined with Augmented Reality
- Comparison of Machine Learning Algorithms for Shelter Animal Classification
- Performance Analysis of Machine Learning Classification Algorithms for Breast Cancer Diagnosis
- Machine Learning Algorithms for The Classification of Cardiovascular Disease- A Comparative Study
- A Survey: Effective Machine Learning Based Classification Algorithm for Medical Dataset
- A Systematic Literature Review on Multi-Label Classification based on Machine Learning Algorithms
- Analysis of Machine Learning Algorithms on Cancer Dataset
- Mammogram Image Classification Using Various Machine Learning Algorithms
- Analysis of Various Machine Learning Algorithms to Predict the Type II Diabetes Disease