arrow
Return

Self-calibration algorithm in an asynchronous P300-based brain-computer interface

delete2014-05-19
delete22
PRE
AI
F
Francesca Schettini *
F
Fabio Aloise
P
Pietro Aricò
D
Donatella Mattia
F
Febo Cincotti
DOI:10.1088/1741-2560/11/3/035004delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Objective. Reliability is a desirable characteristic of brain-computer interface (BCI) systems when they are intended to be used under non-experimental operating conditions. In addition, their overall usability is influenced by the complex and frequent procedures that are required for configuration and calibration. Earlier studies examined the issue of asynchronous control in P300-based BCIs, introducing dynamic stopping and automatic control suspension features. This report proposes and evaluates an algorithm for the automatic recalibration of the classifier's parameters using unsupervised data. Approach. Ten healthy subjects participated in five P300-based BCI sessions throughout a single day. First, we examined whether continuous adaptation of control parameters improved the accuracy of the asynchronous system over time. Then, we assessed the performance of the self-calibration algorithm with respect to the no-recalibration and supervised calibration conditions with regard to system accuracy and communication efficiency. Main results. Offline tests demonstrated that continuous adaptation of the control parameters significantly increased the communication efficiency of asynchronous P300-based BCIs. The self-calibration algorithm correctly assigned labels to unsupervised data with 95% accuracy, effecting communication efficiency that was comparable with that of supervised repeated calibration. Significance. Although additional online tests that involve end-users under non-experimental conditions are needed, these preliminary results are encouraging, from which we conclude that the self-calibration algorithm is a promising solution to improve P300-based BCI usability and reliability.
Keywords:
brain-computer interface (BCI)
asynchronous control
self-calibration algorithm
unsupervised calibration
P300 event-related potential
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Journal of Neural Engineering cover
Journal of Neural Engineering
IF:
3.8
Papers:
4.0K
Citations:
1.4W

Organization

I
irccs santa lucia
Scholars:
3.3K
Papers: 2.1K
Citations: 2
S
sapienza university rome
Scholars:
6.3W
Papers: 4.7W
Citations: 381
Cited Papers

Cited Papers

Mid-Infrared Emission of Transition Metal Co2+-Doped ZnSe Nanocrystals at Room Temperature via Hydrothermal Preparation
err2019-04-03
err0
PREAI
errMeiling Chen; Xiaoxia Cui; Xusheng Xiao; Yantao Xu; Jian Cui; Junjiang Guo; Chao Liu; Haitao Guo
errShare
errSave
errShare
errSave
errShare
errSave
Electrochemical fabrication of large-area Au/TiO2 junctions
err2003-06-01
err0
PREAI
errJing Tang; Matt White; Galen D. Stucky; Eric W. McFarland
errShare
errSave
Interactions of Lemon, Sucrose and Citric Acid in Enhancing Citrus, Sweet and Sour Flavors
err2017-10-09
err0
errOAAI
errMaria G Veldhuizen; Ashik Siddique; Sage Rosenthal; Lawrence E Marks
errShare
errSave
P300-based brain-computer interface for environmental control: an asynchronous approach
err2011-03-24
err83
PREAI
errAloise, F.; Schettini, F.; Arico, P.; Leotta, F.; Salinari, S.; Mattia, D.; Babiloni, F.; Cincotti, F.
errShare
errSave
errShare
errSave
researcher View more