scholarly journals Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning

PLoS ONE ◽  
2012 ◽  
Vol 7 (12) ◽  
pp. e51077 ◽  
Author(s):  
Martin Spüler ◽  
Wolfgang Rosenstiel ◽  
Martin Bogdan
Keyword(s):  
Unsupervised Learning ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Related Potentials ◽  
Online Adaptation

Feature selection method based on Menger curvature and LDA theory for a P300 brain-computer interface

2021 ◽  
Author(s):  
ShuRui Li ◽  
Jing Jin ◽  
Ian Daly ◽  
Chang Liu ◽  
Andrzej Cichocki
Keyword(s):  
Feature Selection ◽  
Brain Computer Interface ◽  
Feature Selection Method ◽  
Event Related Potentials ◽  
Selection Method ◽  
Computer Interface ◽  
Feature Subset ◽  
Linear Discriminant ◽  
Related Potentials ◽  
Menger Curvature

Abstract Brain–computer interface (BCI) systems decode electroencephalogram signals to establish a channel for direct interaction between the human brain and the external world without the need for muscle or nerve control. The P300 speller, one of the most widely used BCI applications, presents a selection of characters to the user and performs character recognition by identifying P300 event-related potentials from the EEG. Such P300-based BCI systems can reach good levels of accuracy but are difficult to use in day-to-day life due to redundancy and noisy signal. A room for improvement should be considered. We propose a novel hybrid feature selection method for the P300-based BCI system to address the problem of feature redundancy, which combines the Menger curvature and linear discriminant analysis. First, selected strategies are applied separately to a given dataset to estimate the gain for application to each feature. Then, each generated value set is ranked in descending order and judged by a predefined criterion to be suitable in classification models. The intersection of the two approaches is then evaluated to identify an optimal feature subset. The proposed method is evaluated using three public datasets, i.e., BCI Competition III dataset II, BNCI Horizon dataset, and EPFL dataset. Experimental results indicate that compared with other typical feature selection and classification methods, our proposed method has better or comparable performance. Additionally, our proposed method can achieve the best classification accuracy after all epochs in three datasets. In summary, our proposed method provides a new way to enhance the performance of the P300-based BCI speller.

scholarly journals Towards the detection of error-related potentials and its integration in the context of a P300 speller brain–computer interface

2012 ◽  
Vol 80 ◽  
pp. 73-82 ◽  
Author(s):  
A. Combaz ◽  
N. Chumerin ◽  
N.V. Manyakov ◽  
A. Robben ◽  
J.A.K. Suykens ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
P300 Speller ◽  
Related Potentials

scholarly journals A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface

2020 ◽  
Vol 191 ◽  
pp. 105419 ◽  
Author(s):  
Francesco Ferracuti ◽  
Valentina Casadei ◽  
Ilaria Marcantoni ◽  
Sabrina Iarlori ◽  
Laura Burattini ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Source Separation ◽  
Computer Interface ◽  
Separation Algorithm ◽  
Related Potentials

scholarly journals A P300 Brain-Computer Interface With a Reduced Visual Field

2020 ◽  
Vol 14 ◽  
Author(s):  
Luiza Kirasirova ◽  
Vladimir Bulanov ◽  
Alexei Ossadtchi ◽  
Alexander Kolsanov ◽  
Vasily Pyatin ◽  
...  
Keyword(s):  
Visual Field ◽  
Visual Processing ◽  
Classification Accuracy ◽  
Peripheral Vision ◽  
Brain Computer Interface ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Central Visual Field ◽  
P300 Speller ◽  
Related Potentials

A P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from event-related potentials (ERPs). In a popular implementation, called P300 speller, a subject looks at a display where characters are flashing and selects one character by attending to it. The selection is recognized as the item with the strongest ERP. The speller performs well when cortical responses to target and non-target stimuli are sufficiently different. Although many strategies have been proposed for improving the BCI spelling, a relatively simple one received insufficient attention in the literature: reduction of the visual field to diminish the contribution from non-target stimuli. Previously, this idea was implemented in a single-stimulus switch that issued an urgent command like stopping a robot. To tackle this approach further, we ran a pilot experiment where ten subjects operated a traditional P300 speller or wore a binocular aperture that confined their sight to the central visual field. As intended, visual field restriction resulted in a replacement of non-target ERPs with EEG rhythms asynchronous to stimulus periodicity. Changes in target ERPs were found in half of the subjects and were individually variable. While classification accuracy was slightly better for the aperture condition (84.3 ± 2.9%, mean ± standard error) than the no-aperture condition (81.0 ± 2.6%), this difference was not statistically significant for the entire sample of subjects (N = 10). For both the aperture and no-aperture conditions, classification accuracy improved over 4 days of training, more so for the aperture condition (from 72.0 ± 6.3% to 87.0 ± 3.9% and from 72.0 ± 5.6% to 97.0 ± 2.2% for the no-aperture and aperture conditions, respectively). Although in this study BCI performance was not substantially altered, we suggest that with further refinement this approach could speed up BCI operations and reduce user fatigue. Additionally, instead of wearing an aperture, non-targets could be removed algorithmically or with a hybrid interface that utilizes an eye tracker. We further discuss how a P300 speller could be improved by taking advantage of the different physiological properties of the central and peripheral vision. Finally, we suggest that the proposed experimental approach could be used in basic research on the mechanisms of visual processing.

scholarly journals Physiological and Medico-Social Research Trends of the Wave P300 and More Late Components of Visual Event-Related Potentials

2020 ◽  
Author(s):  
Sergey Lytaev ◽  
Irina Vatamaniuk
Keyword(s):  
Social Research ◽  
Brain Computer Interface ◽  
Sensory System ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Visual Images ◽  
Physiological Mechanisms ◽  
Multiple Modalities ◽  
Related Potentials ◽  
Sensory Processes

The objective of this study was aimed to study the sensory processes of the “human-computer interaction” model when classifying visual images with an incomplete set of signs based on the analysis of early, middle, late and slow components of event-related potentials (ERPs). 26 healthy subjects (men) aged 20-22 years were investigated. ERPs in 19 monopolar sites according to the 10/20 system were recorded. Discriminant and factor analysis were applied. The component N450 is the most specialized indicator of the perception of unrecognizable (oddball) visual images. The amplitude of the ultra-late components N750 and N900 is also higher under conditions of presentation of the oddball image, regardless of the location of the registration points. The results of the study are discussed in the light of the paradigm of the P300 wave application in brain-computer interface systems, as well as with the peculiarities in brain pathology. Promising directions for the development of studies of the “Brain Computer Interface” (BCI) P300 systems are to increase the throughput of information flows. To extend the application of the P300 ERPs to multiple modalities, the underlying physiological mechanisms and responses of the brain for a particular sensory system and mental function must be carefully examined.

scholarly journals Difference in somatosensory event-related potentials in the blind subjects leads to better performance in tactile P300 BCI

2020 ◽  
Author(s):  
Rafael Grigoryan ◽  
Dariya Goranskaya ◽  
Andrey Demchinsky ◽  
Ksenia Ryabova ◽  
Denis Kuleshov ◽  
...  
Keyword(s):  
Cognitive Processing ◽  
Vision Loss ◽  
Reading Speed ◽  
Brain Computer Interface ◽  
Event Related Potentials ◽  
Individual Performance ◽  
Computer Interface ◽  
Tactile Stimuli ◽  
Related Potentials ◽  
Sensory Compensation

AbstractIn this study, we have created an 8-command P300 tactile BCI with two stimuli types, running on a minimally modified consumer Braille display and tested it on 10 blind subjects and 10 sighted controls. Blind subjects have demonstrated 27% higher median accuracy than sighted controls (p < 0.05), proving that the blind subjects are not only able to use tactile BCI but also can achieve superior results in comparison with sighted subjects. Median accuracy in the blind group with the best stimuli type has reached 95%. The difference in event-related potentials between groups is located in frontocentral sites before 300 ms post-stimulus and corresponds with early cognitive ERP components. The blind subjects have higher amplitude and lower latency of ERPs. This result is consistent through experimental conditions with different tactile stimuli. The classification performance for the blind subjects is correlated with Braille reading speed. This enables a discussion about mechanisms of plastic changes during sensory compensation after vision loss and its dependence on personal perceptual experience.Author summarySensory compensation following vision loss can be recognized as a unique model for neural plasticity. However, the magnitude of the effect and the specific tasks where it’s manifested is still a subject for debate. In this study, we have created a tactile brain-computer interface game to study how somatosensory processing is different between the blind and the sighted people. The participants were required to attend to tactile stimuli, and the correct stimulus was selected using realtime EEG classification. We have shown, for the first time, that the blind subjects are significantly better than the sighted in tactile brain-computer interface tasks. We have also found, that individual performance is correlated with Braille proficiency. This result links personal perceptual abilities in two different sensory tasks. EEG analysis revealed that differences in performance can be attributed to early cognitive processing steps. Along with practical considerations in brain-computer interface development, the results also add to the data on cognitive processing in the blind and enable the discussion on the importance of Braille education.

Sign in / Sign up

Export Citation Format

Share Document