Literature DB >> 28760486

A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study.

Carlos P Amaral1, Marco A Simões1, Susana Mouga1, João Andrade1, Miguel Castelo-Branco2.   

Abstract

BACKGROUND: We present a novel virtual-reality P300-based Brain Computer Interface (BCI) paradigm using social cues to direct the focus of attention. We combined interactive immersive virtual-reality (VR) technology with the properties of P300 signals in a training tool which can be used in social attention disorders such as autism spectrum disorder (ASD). NEW
METHOD: We tested the novel social attention training paradigm (P300-based BCI paradigm for rehabilitation of joint-attention skills) in 13 healthy participants, in 3 EEG systems. The more suitable setup was tested online with 4 ASD subjects. Statistical accuracy was assessed based on the detection of P300, using spatial filtering and a Naïve-Bayes classifier.
RESULTS: We compared: 1 - g.Mobilab+ (active dry-electrodes, wireless transmission); 2 - g.Nautilus (active electrodes, wireless transmission); 3 - V-Amp with actiCAP Xpress dry-electrodes. Significant statistical classification was achieved in all systems. g.Nautilus proved to be the best performing system in terms of accuracy in the detection of P300, preparation time, speed and reported comfort. Proof of concept tests in ASD participants proved that this setup is feasible for training joint attention skills in ASD. COMPARISON WITH EXISTING
METHODS: This work provides a unique combination of 'easy-to-use' BCI systems with new technologies such as VR to train joint-attention skills in autism.
CONCLUSIONS: Our P300 BCI paradigm is feasible for future Phase I/II clinical trials to train joint-attention skills, with successful classification within few trials, online in ASD participants. The g.Nautilus system is the best performing one to use with the developed BCI setup.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Brain-computer interface; Dry electrodes; EEG; Social attention; Virtual reality

Mesh:

Year:  2017        PMID: 28760486     DOI: 10.1016/j.jneumeth.2017.07.029

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  13 in total

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Review 2.  Progress in Brain Computer Interface: Challenges and Opportunities.

Authors:  Simanto Saha; Khondaker A Mamun; Khawza Ahmed; Raqibul Mostafa; Ganesh R Naik; Sam Darvishi; Ahsan H Khandoker; Mathias Baumert
Journal:  Front Syst Neurosci       Date:  2021-02-25

3.  A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision.

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Journal:  Front Hum Neurosci       Date:  2021-07-08       Impact factor: 3.169

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5.  A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface.

Authors:  Carlos Amaral; Susana Mouga; Marco Simões; Helena C Pereira; Inês Bernardino; Hugo Quental; Rebecca Playle; Rachel McNamara; Guiomar Oliveira; Miguel Castelo-Branco
Journal:  Front Neurosci       Date:  2018-07-13       Impact factor: 4.677

6.  Head-Down Tilt Position, but Not the Duration of Bed Rest Affects Resting State Electrocortical Activity.

Authors:  Katharina Brauns; Anika Friedl-Werner; Martina A Maggioni; Hanns-Christian Gunga; Alexander C Stahn
Journal:  Front Physiol       Date:  2021-02-24       Impact factor: 4.566

7.  Are the new mobile wireless EEG headsets reliable for the evaluation of musical pleasure?

Authors:  Thibault Chabin; Damien Gabriel; Emmanuel Haffen; Thierry Moulin; Lionel Pazart
Journal:  PLoS One       Date:  2020-12-31       Impact factor: 3.240

8.  A neurotechnological aid for semi-autonomous suction in robotic-assisted surgery.

Authors:  Juan Antonio Barragan; Jing Yang; Denny Yu; Juan P Wachs
Journal:  Sci Rep       Date:  2022-03-16       Impact factor: 4.379

9.  Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors.

Authors:  Javier Marín-Morales; Juan Luis Higuera-Trujillo; Alberto Greco; Jaime Guixeres; Carmen Llinares; Enzo Pasquale Scilingo; Mariano Alcañiz; Gaetano Valenza
Journal:  Sci Rep       Date:  2018-09-12       Impact factor: 4.379

10.  Prediction in Autism by Deep Learning Short-Time Spontaneous Hemodynamic Fluctuations.

Authors:  Lingyu Xu; Xiulin Geng; Xiaoyu He; Jun Li; Jie Yu
Journal:  Front Neurosci       Date:  2019-11-08       Impact factor: 4.677

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