Detecting attention breakdowns in robotic neurofeedback systems

Parisa Nahaltahmasebi; Mohamed Chetouani; David Cohen; Salvatore Anzalone

Detecting attention breakdowns in robotic neurofeedback systems

Parisa Nahaltahmasebi, Mohamed Chetouani, David Cohen, Salvatore Anzalone

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper focuses on the EEG measures suitable for robotic neurofeedback systems, able to detect and intervene in case of attention breakdowns. Such systems can be useful tools in cognitive remediation of children with ASD, in particular to improve their attention span. The proposed study focuses on a particular EEG measure, the Theta/Beta ratio, as convenient feature for recognizing attention states in scenarios of cognitive effort. A setup for the validation of this measure employing pupil dilation as attention baseline is introduced. Results show coherence between the Theta/Beta ratio and the pupil dilation.

Original language	English
Pages (from-to)	41-46
Number of pages	6
Journal	CEUR Workshop Proceedings
Volume	2054
State	Published - 2018
Externally published	Yes
Event	4th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2017 - Bari, Italy Duration: 14 Nov 2017 → 15 Nov 2017

Keywords

Attention
Neurofeedback
Social robots.

Cite this

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title = "Detecting attention breakdowns in robotic neurofeedback systems",

abstract = "This paper focuses on the EEG measures suitable for robotic neurofeedback systems, able to detect and intervene in case of attention breakdowns. Such systems can be useful tools in cognitive remediation of children with ASD, in particular to improve their attention span. The proposed study focuses on a particular EEG measure, the Theta/Beta ratio, as convenient feature for recognizing attention states in scenarios of cognitive effort. A setup for the validation of this measure employing pupil dilation as attention baseline is introduced. Results show coherence between the Theta/Beta ratio and the pupil dilation.",

keywords = "Attention, Neurofeedback, Social robots.",

author = "Parisa Nahaltahmasebi and Mohamed Chetouani and David Cohen and Salvatore Anzalone",

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year = "2018",

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TY - JOUR

T1 - Detecting attention breakdowns in robotic neurofeedback systems

AU - Nahaltahmasebi, Parisa

AU - Chetouani, Mohamed

AU - Cohen, David

AU - Anzalone, Salvatore

PY - 2018

Y1 - 2018

N2 - This paper focuses on the EEG measures suitable for robotic neurofeedback systems, able to detect and intervene in case of attention breakdowns. Such systems can be useful tools in cognitive remediation of children with ASD, in particular to improve their attention span. The proposed study focuses on a particular EEG measure, the Theta/Beta ratio, as convenient feature for recognizing attention states in scenarios of cognitive effort. A setup for the validation of this measure employing pupil dilation as attention baseline is introduced. Results show coherence between the Theta/Beta ratio and the pupil dilation.

AB - This paper focuses on the EEG measures suitable for robotic neurofeedback systems, able to detect and intervene in case of attention breakdowns. Such systems can be useful tools in cognitive remediation of children with ASD, in particular to improve their attention span. The proposed study focuses on a particular EEG measure, the Theta/Beta ratio, as convenient feature for recognizing attention states in scenarios of cognitive effort. A setup for the validation of this measure employing pupil dilation as attention baseline is introduced. Results show coherence between the Theta/Beta ratio and the pupil dilation.

KW - Attention

KW - Neurofeedback

KW - Social robots.

UR - http://www.scopus.com/inward/record.url?scp=85045626830&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85045626830

SN - 1613-0073

VL - 2054

SP - 41

EP - 46

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 4th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2017

Y2 - 14 November 2017 through 15 November 2017

ER -

Detecting attention breakdowns in robotic neurofeedback systems

Abstract

Keywords

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Cite this