Publication

A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces

Touradj Ebrahimi, Jean-Marc Vesin
2005
Conference Papers

Abstract

Gradient boosting is a machine learning method, that builds one strong classifier from many weak classifiers. In this work, an algorithm based on gradient boosting is presented, that detects event-related potentials in single electroencephalogram (EEG) trials. The algorithm is used to detect the P300 in the human EEG and to build a brain-computer interface (BCI), specifically a spelling device. Important features of the method described here are its high classification accuracy and its conceptual simplicity. The algorithm was tested with datasets recorded in our lab and one benchmark dataset from the BCI Competition 2003. The number of correctly inferred symbols with the P300 speller paradigm varied between 90% and 100%. In particular, all of the inferred symbols were correct for the BCI competition dataset.

Official source

https://infoscience.epfl.ch/entities/publication/39f2509e-d1cb-43bb-8ab8-76efd38650df

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Touradj Ebrahimi, Jean-Marc Vesin
2005
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/39f2509e-d1cb-43bb-8ab8-76efd38650df

About this result

Related concepts (32)

Brain–computer interface

A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI) or smartbrain, is a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary component of the physical movement of body parts, although they also raise the possibility of the erasure of the discreteness of brain and machine.

Boosting (machine learning)

In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).

AdaBoost

AdaBoost, short for Adaptive Boosting, is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the 2003 Gödel Prize for their work. It can be used in conjunction with many other types of learning algorithms to improve performance. The output of the other learning algorithms ('weak learners') is combined into a weighted sum that represents the final output of the boosted classifier. Usually, AdaBoost is presented for binary classification, although it can be generalized to multiple classes or bounded intervals on the real line.

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A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces

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