Literature DB >> 23673460

Discrimination between control and idle states in asynchronous SSVEP-based brain switches: a pseudo-key-based approach.

Jiahui Pan1, Yuanqing Li, Rui Zhang, Zhenghui Gu, Feng Li.   

Abstract

A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) can operate as an asynchronous brain switch. When SSVEP is detected with the "on/off" button flickering at a fixed frequency, the subject is identified as in the control state. Otherwise, he is in the idle state. Generally, the detection of the idle/control state is based on a predefined threshold, which is related to power. However, due to the variability of the electroencephalogram (EEG) signal, it is difficult to find an optimal threshold to achieve a high true-positive rate (TPR) in the control state while maintaining a low false-positive rate (FPR) in the idle state. In this paper, a novel pseudo-key-based approach is presented for better discriminating the control and idle states. A dedicated "on/off" button (target key) and several additional buttons (pseudo-keys) are displayed on the graphical user interface (GUI), and all of these buttons flash at different frequencies. The control state is identified from the EEG signal under two conditions. The first is a common thresholding condition, where the power ratio of the target key frequency component to a certain neighboring frequency band is above a predefined threshold. The second is a comparison condition, where the power of the target key frequency component is higher than any of the pseudo-keys. The effectiveness of the proposed approach is validated by several experiments. Further analysis shows that introducing the pseudo-keys can significantly reduce the probability that the SSVEP will be detected in response to the flickering target key in the idle state without substantially affecting the detection in the control state, providing strong evidence in support of our approach.

Mesh:

Year:  2013        PMID: 23673460     DOI: 10.1109/TNSRE.2013.2253801

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  7 in total

1.  Plug&Play Brain-Computer Interfaces for effective Active and Assisted Living control.

Authors:  Niccolò Mora; Ilaria De Munari; Paolo Ciampolini; José Del R Millán
Journal:  Med Biol Eng Comput       Date:  2016-11-17       Impact factor: 2.602

2.  Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs.

Authors:  Jing Jiang; Chunhui Wang; Jinghan Wu; Wei Qin; Minpeng Xu; Erwei Yin
Journal:  Front Hum Neurosci       Date:  2020-06-30       Impact factor: 3.169

3.  Prognosis for patients with cognitive motor dissociation identified by brain-computer interface.

Authors:  Jiahui Pan; Qiuyou Xie; Pengmin Qin; Yan Chen; Yanbin He; Haiyun Huang; Fei Wang; Xiaoxiao Ni; Andrzej Cichocki; Ronghao Yu; Yuanqing Li
Journal:  Brain       Date:  2020-04-01       Impact factor: 13.501

4.  Development of a Brain-Computer Interface Toggle Switch with Low False-Positive Rate Using Respiration-Modulated Photoplethysmography.

Authors:  Chang-Hee Han; Euijin Kim; Chang-Hwan Im
Journal:  Sensors (Basel)       Date:  2020-01-08       Impact factor: 3.576

5.  A Hybrid BCI Based on SSVEP and EOG for Robotic Arm Control.

Authors:  Yuanlu Zhu; Ying Li; Jinling Lu; Pengcheng Li
Journal:  Front Neurorobot       Date:  2020-11-20       Impact factor: 2.650

6.  Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016.

Authors:  Domen Novak; Roland Sigrist; Nicolas J Gerig; Dario Wyss; René Bauer; Ulrich Götz; Robert Riener
Journal:  Front Neurosci       Date:  2018-01-11       Impact factor: 4.677

7.  An Efficient Asynchronous High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface speller: The Problem of Individual Differences.

Authors:  Saba Ajami; Amin Mahnam; Samane Behtaj; Vahid Abootalebi
Journal:  J Med Signals Sens       Date:  2018 Oct-Dec
  7 in total

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