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Towards ambulatory brain-computer interfaces: a pilot study with P300 signals

Published: 29 October 2009 Publication History

Abstract

Brain-Computer Interfaces (BCI) are communication systems that enable users to interact with computers using only brain activity. This activity is generally measured by ElectroEncephaloGraphy (EEG). A major limitation of BCI is the electrical sensitivity of EEG which causes severe deterioration of the signals when the user is moving. This constrains current EEG-based BCI to be used only by sitting and still subjects, hence limiting the use of BCI for applications such as video games. In this paper, we proposed a feasibility study to discover whether a BCI system, here based on the P300 brain signal, could be used with a moving subject. We recorded EEG signals from 5 users in 3 conditions: sitting, standing and walking. Analysis of the recorded signals suggested that despite the noise generated by the user's motion, it was still possible to detect the P300 in the signals in each of the three conditions. This opens new perspective of applications using a wearable P300-based BCI as input device, e.g., for entertainment and video games.

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Cited By

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  • (2024)Machine learning for physical motion identification using EEG signals: a comparative study of classifiers and hyperparameter tuningJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-024-04764-415:4(2455-2466)Online publication date: 11-Mar-2024
  • (2022)Past, Present, and Future of EEG-Based BCI ApplicationsSensors10.3390/s2209333122:9(3331)Online publication date: 26-Apr-2022
  • (2021)Effect of Static Posture on Online Performance of P300-Based BCIs for TV ControlSensors10.3390/s2107227821:7(2278)Online publication date: 24-Mar-2021
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  1. Towards ambulatory brain-computer interfaces: a pilot study with P300 signals

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    cover image ACM Other conferences
    ACE '09: Proceedings of the International Conference on Advances in Computer Entertainment Technology
    October 2009
    456 pages
    ISBN:9781605588643
    DOI:10.1145/1690388
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 29 October 2009

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    Author Tags

    1. P300
    2. ambulatory interface
    3. brain-computer interface (BCI)
    4. electroencephalography (EEG)

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    View all
    • (2024)Machine learning for physical motion identification using EEG signals: a comparative study of classifiers and hyperparameter tuningJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-024-04764-415:4(2455-2466)Online publication date: 11-Mar-2024
    • (2022)Past, Present, and Future of EEG-Based BCI ApplicationsSensors10.3390/s2209333122:9(3331)Online publication date: 26-Apr-2022
    • (2021)Effect of Static Posture on Online Performance of P300-Based BCIs for TV ControlSensors10.3390/s2107227821:7(2278)Online publication date: 24-Mar-2021
    • (2021)Development and Evaluation of a Smartphone-Based Electroencephalography (EEG) SystemIEEE Access10.1109/ACCESS.2021.30799929(75650-75667)Online publication date: 2021
    • (2021)Clustering as a Brain-Network Detection Tool for Mental Imagery IdentificationProceedings of Research and Applications in Artificial Intelligence10.1007/978-981-16-1543-6_8(87-99)Online publication date: 11-Jun-2021
    • (2020)Grand Challenges in Neurotechnology and System NeuroergonomicsFrontiers in Neuroergonomics10.3389/fnrgo.2020.6025041Online publication date: 30-Nov-2020
    • (2020)Motor Imagery Under Distraction— An Open Access BCI DatasetFrontiers in Neuroscience10.3389/fnins.2020.56614714Online publication date: 19-Oct-2020
    • (2020)Riemann-Based Algorithms Assessment for Single- and Multiple-Trial P300 Classification in Non-Optimal EnvironmentsIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2020.304341828:12(2754-2761)Online publication date: Dec-2020
    • (2019)Monitoring Pilot’s Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight ConditionsSensors10.3390/s1906132419:6(1324)Online publication date: 16-Mar-2019
    • (2019)P300 in the park: feasibility of online data acquisition and integration in a Mobile Brain/Body Imaging setting2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)10.1109/NER.2019.8717141(319-322)Online publication date: Mar-2019
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