Identification of Levels of Interest in Political Speech Using EEG Patterns

Sriharsha Ramaraju; Mohammed Roula; Ahmed Izzidien; Charles  Emina

Identification of Levels of Interest in Political Speech Using EEG Patterns

Sriharsha Ramaraju, Mohammed Roula, Ahmed Izzidien, Charles Emina

Allbwn ymchwil: Cyfraniad at gynhadledd › Papur › adolygiad gan gymheiriaid

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In this paper, we present preliminary results of a study on physiologically quantifying electorate responsiveness during political speech. Two subjects watched the opening 15 minutes of the State of the Union speech by US president Barack Obama, while their EEG signals were recorded on 14 channels. The two subjects used a smart phone app to indicate if they found the content of the speech at any given time interesting or not, by pressing one of two buttons. Frequency analysis was performed on data collected. Independent sample t-test was performed on the dataset and features with p-value < 0.0005 were chosen for both the subjects. 98% of the frequencies are lying in beta-higher beta region.

Iaith wreiddiol	Saesneg
Statws	Cyhoeddwyd - Awst 2015
Digwyddiad	37th Annual Conference of the IEEE Engineering in Medicine and Biology Society (2015): Biomedical Engineering - a Bridge to improve the Quality of Health Care and the Quality of Life - MiCo - Milano Conference Center, Milan, Yr Eidal Hyd: 25 Awst 2015 → 29 Awst 2015 http://embc.embs.org/2015/

Cynhadledd

Cynhadledd	37th Annual Conference of the IEEE Engineering in Medicine and Biology Society (2015)
Teitl cryno	EMBC 2015
Gwlad/Tiriogaeth	Yr Eidal
Dinas	Milan
Cyfnod	25/08/15 → 29/08/15
Cyfeiriad rhyngrwyd	http://embc.embs.org/2015/

Dyfynnu hyn

@conference{a90eaf0cfd974c1ba3afff632ee0de11,

title = "Identification of Levels of Interest in Political Speech Using EEG Patterns",

abstract = "In this paper, we present preliminary results of a study on physiologically quantifying electorate responsiveness during political speech. Two subjects watched the opening 15 minutes of the State of the Union speech by US president Barack Obama, while their EEG signals were recorded on 14 channels. The two subjects used a smart phone app to indicate if they found the content of the speech at any given time interesting or not, by pressing one of two buttons. Frequency analysis was performed on data collected. Independent sample t-test was performed on the dataset and features with p-value < 0.0005 were chosen for both the subjects. 98% of the frequencies are lying in beta-higher beta region.",

author = "Sriharsha Ramaraju and Mohammed Roula and Ahmed Izzidien and Charles Emina",

year = "2015",

month = aug,

language = "English",

note = "37th Annual Conference of the IEEE Engineering in Medicine and Biology Society (2015) : Biomedical Engineering - a Bridge to improve the Quality of Health Care and the Quality of Life, EMBC 2015 ; Conference date: 25-08-2015 Through 29-08-2015",

url = "http://embc.embs.org/2015/",

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TY - CONF

T1 - Identification of Levels of Interest in Political Speech Using EEG Patterns

AU - Ramaraju, Sriharsha

AU - Roula, Mohammed

AU - Izzidien, Ahmed

AU - Emina, Charles

PY - 2015/8

Y1 - 2015/8

N2 - In this paper, we present preliminary results of a study on physiologically quantifying electorate responsiveness during political speech. Two subjects watched the opening 15 minutes of the State of the Union speech by US president Barack Obama, while their EEG signals were recorded on 14 channels. The two subjects used a smart phone app to indicate if they found the content of the speech at any given time interesting or not, by pressing one of two buttons. Frequency analysis was performed on data collected. Independent sample t-test was performed on the dataset and features with p-value < 0.0005 were chosen for both the subjects. 98% of the frequencies are lying in beta-higher beta region.

AB - In this paper, we present preliminary results of a study on physiologically quantifying electorate responsiveness during political speech. Two subjects watched the opening 15 minutes of the State of the Union speech by US president Barack Obama, while their EEG signals were recorded on 14 channels. The two subjects used a smart phone app to indicate if they found the content of the speech at any given time interesting or not, by pressing one of two buttons. Frequency analysis was performed on data collected. Independent sample t-test was performed on the dataset and features with p-value < 0.0005 were chosen for both the subjects. 98% of the frequencies are lying in beta-higher beta region.

M3 - Paper

T2 - 37th Annual Conference of the IEEE Engineering in Medicine and Biology Society (2015)

Y2 - 25 August 2015 through 29 August 2015

ER -

Identification of Levels of Interest in Political Speech Using EEG Patterns

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