Incorporation of Inter-Subject Information to Improve the Accuracy of Subject-Specific P300 Classifiers

2016 ◽  
Vol 26 (03) ◽  
pp. 1650010 ◽  
Author(s):  
Minpeng Xu ◽  
Jing Liu ◽  
Long Chen ◽  
Hongzhi Qi ◽  
Feng He ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Heterogeneous Data ◽  
Computer Interface ◽  
Training Dataset ◽  
Comprehensive Investigation ◽  
P300 Speller ◽  
Classifier Calibration ◽  
Accuracy And Precision ◽  
Subject Specific ◽  
The Subject

Although the inter-subject information has been demonstrated to be effective for a rapid calibration of the P300-based brain–computer interface (BCI), it has never been comprehensively tested to find if the incorporation of heterogeneous data could enhance the accuracy. This study aims to improve the subject-specific P300 classifier by adding other subject’s data. A classifier calibration strategy, weighted ensemble learning generic information (WELGI), was developed, in which elementary classifiers were constructed by using both the intra- and inter-subject information and then integrated into a strong classifier with a weight assessment. 55 subjects were recruited to spell 20 characters offline using the conventional P300-based BCI, i.e. the P300-speller. Four different metrics, the P300 accuracy and precision, the round accuracy, and the character accuracy, were performed for a comprehensive investigation. The results revealed that the classifier constructed on the training dataset in combination with adding other subject’s data was significantly superior to that without the inter-subject information. Therefore, the WELGI is an effective classifier calibration strategy which uses the inter-subject information to improve the accuracy of subject-specific P300 classifiers, and could also be applied to other BCI paradigms.

Extending brain-computer interface access with a multilingual language model in the P300 speller

2021 ◽  
pp. 1-13
Author(s):  
P Loizidou ◽  
E Rios ◽  
A Marttini ◽  
O Keluo-Udeke ◽  
J Soetedjo ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Language Model ◽  
Computer Interface ◽  
P300 Speller

scholarly journals Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Robert Leeb ◽  
Doron Friedman ◽  
Gernot R. Müller-Putz ◽  
Reinhold Scherer ◽  
Mel Slater ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Beta Oscillations ◽  
Brain Waves ◽  
The Subject ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
Spinal Cord Injured

The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG) by imagination of movements of his paralyzed feet. These beta oscillations were used for a self-paced (asynchronous) brain-computer interface (BCI) control based on a single bipolar EEG recording. The subject was placed inside a virtual street populated with avatars. The task was to “go” from avatar to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the participant was able to successfully perform this asynchronous experiment with a performance of 90%, single runs up to 100%.

scholarly journals A P300 brain-computer interface with a reduced visual field

2020 ◽  
Author(s):  
Luiza Kirasirova ◽  
Vladimir Bulanov ◽  
Alexei Ossadtchi ◽  
Alexander Kolsanov ◽  
Vasily Pyatin ◽  
...  
Keyword(s):  
Visual Field ◽  
Visual Processing ◽  
Peripheral Vision ◽  
Brain Computer Interface ◽  
Basic Research ◽  
Computer Interface ◽  
Eye Tracker ◽  
Central Visual Field ◽  
P300 Speller ◽  
User Fatigue

AbstractA P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from visual evoked potentials (VEPs). 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 by the strongest VEP. 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 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. To tackle this approach further, we ran a pilot experiment where ten subjects first operated a traditional P300 speller and then wore a binocular aperture that confined their sight to the central visual field. Visual field restriction resulted in a reduction of non-target responses in all subjects. Moreover, in four subjects, target-related VEPs became more distinct. We suggest that 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 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 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.

Brain Computer Interface for Communication and Control of Peripherals and Appliances

2020 ◽  
Vol 17 (4) ◽  
pp. 1616-1621
Author(s):  
K. Sathish ◽  
Aritra Paul ◽  
Debapriya Roy ◽  
Ishmeet Kalra ◽  
Simran Bajaj
Keyword(s):  
Brain Computer Interface ◽  
Processing System ◽  
Audio Signal ◽  
Computer Interface ◽  
Single Frequency ◽  
Discrete Wavelet ◽  
Continuous Eeg ◽  
Transmission Reduction ◽  
The Subject ◽  
Simplified Solution

The concept is designed to improve upon the recent developed system, utilizing auditory steady state response (ASSR) as a basis for the Brain Computer Interface (BCI) paradigm. It utilizes the classification of signals through a discrete wavelet transform (DWT) before the actual transmission to reduce overhead at the processing system. The electroencephalogram (EEG) obtained from the subject is through a p300 based EEG receivers. A compression algorithm is used to reduce the bandwidth usage and provide a quicker transmission of the large and continuous EEG. An Arduino board along with a proximity sensor is used to detect the presence and distance of the subject and consequently control playback of a single frequency audio signal, which as received by the user, is used for producing the EEG signals. A continuous focus of the user is required on the playback of the single frequency sound to produce a sizeable reading. At the receiving end, another Arduino board is installed with an SD card module, which contains the commands, responsible for the actual control of the devices. The concept can be utilized for various purposes from controlling IoT based systems to wheelchairs and hospital beds as well as bionic limbs, which however are limited due to the overall bulk of all the equipment currently required. The main aim of this paper is to propose an improvement in the transmission, reduction the latency of the signals and to provide a concept for utilization by the handicapped or physically impaired patients. Since the EEG is obtained through the inner ear of the subject, it completely eliminates any need for invasive surgery and provides a simplified solution. Developments have shown to be able to achieve over 95% of accuracy in the domain, currently limited by length of the EEG required in order to process the actual commands from the subject’s brain.

scholarly journals A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental Tasks

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Tao Geng ◽  
John Q. Gan ◽  
Matthew Dyson ◽  
Chun SL Tsui ◽  
Francisco Sepulveda
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Classification Algorithms ◽  
Single Trial ◽  
Linear Discriminant ◽  
The Subject ◽  
Offline Analysis ◽  
Binary Classifiers ◽  
Novel Design ◽  
Mental Tasks

A novel 4-class single-trial brain computer interface (BCI) based on two (rather than four or more) binary linear discriminant analysis (LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms.

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