P300 Habituation Patterns: Individual Differences from Ultradian Rhythms

Lin, Eric W.; Polich, John

doi:10.2466/pms.1999.88.3c.1111

Cited by 16 publications

(2 citation statements)

References 54 publications

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“…However, these approaches relied on estimating threshold parameters based on a participant pool average or using the same parameters across users. A user's performance can vary acutely based signal SNR, artifact level, attention, mood etc [44,45] and there is inter-participant variability in performance [46]. Alternatively, a probabilistic-based stopping criterion allows for a more flexible means to adapt data collection based on changes in a user's acute performance as this uncertainty is captured via classifier score distributions.…”

Section: Discussionmentioning

confidence: 99%

Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study

et al. 2015

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Objective The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. Approach We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute signal-to-noise ratio of a user’s electroencephalography data. We further enhanced the algorithm by incorporating information about the user’s language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. Main Results Results from online testing of the dynamic stopping algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/sec (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the dynamic stopping algorithms. Significance We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.

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Section: Discussionmentioning

confidence: 99%

Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study

et al. 2015

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“…In clinical practice, these potentials are recorded using electrodes which are positioned on the skull surface, according with the International System 10–20 (SI 10–20) of the American Society of Eletroencephalography 7 . Nonetheless, there is no consensus in relation to the number and positioning of the live electrodes, having seen that some authors use only one live electrode placed on Cz 8 , 9 , 10 , 11 , others use two live electrodes, placed on Fz and Cz 12 , 3 , 13 , Fz and Pz 14 , Cz and Pz 15 , 16 , or even three live electrodes, placed on Fz, Cz and Pz 17 .…”

Section: Introductionmentioning

confidence: 99%