Nath, SS;
Mukhopadhyay, D;
Miyapuram, KP;
(2019)
Emotive Stimuli-triggered Participant-based Clustering Using a Novel Split-and-Merge Algorithm.
In:
Proceedings of the ACM India Joint International Conference on Data Science and Management of Data.
(pp. pp. 277-280).
The Association for Computing Machinery
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Abstract
EEG signal analysis is a powerful technique to decode the activities of the human brain. Emotion detection among individuals using EEG is often reported to classify people based on emotions. We questioned this observation and hypothesized that different people respond differently to emotional stimuli and have an intrinsic predisposition to respond. We designed experiments to study the responses of participants to various emotional stimuli in order to compare participant-wise categorization to emotion-wise categorization of the data. The experiments were conducted on a homogeneous set of 20 participants by administering 9 short, one to two minute movie clips depicting different emotional content. The EEG signal data was recorded using the 128 channel high density geodesic net. The data was filtered, segmented, converted to frequency domain and alpha, beta and theta ranges were extracted. Clustering was performed using a novel recursive-split and merge unsupervised algorithm. The data was analyzed through confusion matrices, plots and normalization techniques. It was found that the variation in emotive responses of a participant was significantly lower than the variation across participants. This resulted in more efficient participant-based clustering as compared to emotive stimuli-based clustering. We concluded that the emotive response is perhaps a signature of an individual with a characteristic pattern of EEG signals. Our findings on further experimentation will prove valuable for the progress of research in cognitive sciences, security and other related areas.
| Type: | Proceedings paper |
|---|---|
| Title: | Emotive Stimuli-triggered Participant-based Clustering Using a Novel Split-and-Merge Algorithm |
| Event: | The ACM India Joint International Conference on Data Science and Management of Data |
| Location: | Kolkata, India |
| Dates: | 3rd-5th January 2019 |
| ISBN-13: | 978-1-4503-6207-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3297001.3297040 |
| Publisher version: | https://doi.org/10.1145/3297001.3297040 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Clustering, EEG, Emotional Recognition, K-means, Brain Computer Interface |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10066178 |
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