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

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Publication status	Published - Aug 2015
Event	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, Italy Duration: 25 Aug 2015 → 29 Aug 2015 http://embc.embs.org/2015/

Conference

Conference	37th Annual Conference of the IEEE Engineering in Medicine and Biology Society (2015)
Abbreviated title	EMBC 2015
Country/Territory	Italy
City	Milan
Period	25/08/15 → 29/08/15
Internet address	http://embc.embs.org/2015/

Cite this

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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",

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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

Abstract

Conference

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