Publication

On the Need for On-Line Learning in Brain-Computer Interfaces

2004
Conference Papers

Abstract

In this paper we motivate the need for on-line learning in brain-computer interfaces (BCI) and illustrate its benefits with the simplest method, namely fixed learning rates. However, the use of this method is supported by the risk of hampering the user to acquire suitable control of the BCI if the embedded classifier changes too rapidly. We report the results with 3 beginner subjects in a series of consecutive recordings, where the classifiers are iteratively trained with the data of a given session and tested on the next session. Interestingly, performance improved over sessions significantly for 2 of the subjects. These results show that on-line learning improves systematically the performance of the subjects. Moreover, performance with on-line learning is statistically similar to that obtained training the classifier off-line with the same amount of data.

Official source

https://infoscience.epfl.ch/entities/publication/0cb61bf7-7c42-426f-9878-56425265f68f

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2004
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/0cb61bf7-7c42-426f-9878-56425265f68f

About this result

Related concepts (24)

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

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.

Online machine learning

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.

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On the Need for On-Line Learning in Brain-Computer Interfaces

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