3-D Interface for the P300 Speller BCI
نویسندگان
چکیده
منابع مشابه
Semi-Supervised Clustering Approach for P300 based BCI Speller Systems
The paper presents a k-means based semi-supervised clustering approach for recognizing and classifying P300 signals for BCI Speller System. P300 signals are proved to be the most suitable Event Related Potential (ERP) signal, used to develop the BCI systems. Due to non-stationary nature of ERP signals, the wavelet transform is the best analysis tool for extracting informative features from P300...
متن کاملAdaptive training session for a P300 speller brain-computer interface.
With a brain-computer interface (BCI), it is nowadays possible to achieve a direct pathway between the brain and computers thanks to the analysis of some particular brain activities. The detection of even-related potentials, like the P300 in the oddball paradigm exploited in P300-speller, provides a way to create BCIs by assigning several detected ERP to a command. Due to the noise present in t...
متن کاملSensors selection for P300 speller brain computer interface
Brain-computer interfaces (BCI) are communication system that use brain activities to control a device. The BCI studied is based on the P300 speller [1]. A new algorithm to select relevant sensors is proposed: it is based on a previous proposed algorithm [2] used to enhance P300 potentials by spatial filters. Data recorded on three subjects were used to evaluate the proposed selection method: i...
متن کاملSensor selection for P300 speller brain computer interface
Brain-computer interfaces (BCI) are communication system that use brain activities to control a device. The BCI studied is based on the P300 speller [1]. A new algorithm to select relevant sensors is proposed: it is based on a previous proposed algorithm [2] used to enhance P300 potentials by spatial filters. Data recorded on three subjects were used to evaluate the proposed selection method: i...
متن کاملEnsemble SWLDA Classifiers for the P300 Speller
The P300 Speller has proven to be an effective paradigm for braincomputer interface (BCI) communication. Using this paradigm, studies have shown that a simple linear classifier can perform as well as more complex nonlinear classifiers. Several studies have examined methods such as Fisher’s Linear Discriminant (FLD), Stepwise Linear Discriminant Analysis (SWLDA), and Support Vector Machines (SVM...
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ژورنال
عنوان ژورنال: IEEE Transactions on Human-Machine Systems
سال: 2020
ISSN: 2168-2291,2168-2305
DOI: 10.1109/thms.2020.3016079