A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing
In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classificatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2480 |
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| author | Vangelis P. Oikonomou Kostas Georgiadis Fotis Kalaganis Spiros Nikolopoulos Ioannis Kompatsiaris |
| author_facet | Vangelis P. Oikonomou Kostas Georgiadis Fotis Kalaganis Spiros Nikolopoulos Ioannis Kompatsiaris |
| author_sort | Vangelis P. Oikonomou |
| collection | DOAJ |
| description | In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy). |
| first_indexed | 2024-03-11T07:10:15Z |
| format | Article |
| id | doaj.art-3ff61a6f674347ee934f7e3e99e26e23 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:10:15Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3ff61a6f674347ee934f7e3e99e26e232023-11-17T08:35:22ZengMDPI AGSensors1424-82202023-02-01235248010.3390/s23052480A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in NeuromarketingVangelis P. Oikonomou0Kostas Georgiadis1Fotis Kalaganis2Spiros Nikolopoulos3Ioannis Kompatsiaris4Information Technologies Institute, Centre for Research and Technology Hellas, CERTH-ITI, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, GreeceInformation Technologies Institute, Centre for Research and Technology Hellas, CERTH-ITI, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, GreeceInformation Technologies Institute, Centre for Research and Technology Hellas, CERTH-ITI, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, GreeceInformation Technologies Institute, Centre for Research and Technology Hellas, CERTH-ITI, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, GreeceInformation Technologies Institute, Centre for Research and Technology Hellas, CERTH-ITI, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, GreeceIn this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).https://www.mdpi.com/1424-8220/23/5/2480sparse representation classificationbrain computer interfacesneuromarketingelectroencephalography |
| spellingShingle | Vangelis P. Oikonomou Kostas Georgiadis Fotis Kalaganis Spiros Nikolopoulos Ioannis Kompatsiaris A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing Sensors sparse representation classification brain computer interfaces neuromarketing electroencephalography |
| title | A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing |
| title_full | A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing |
| title_fullStr | A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing |
| title_full_unstemmed | A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing |
| title_short | A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing |
| title_sort | sparse representation classification scheme for the recognition of affective and cognitive brain processes in neuromarketing |
| topic | sparse representation classification brain computer interfaces neuromarketing electroencephalography |
| url | https://www.mdpi.com/1424-8220/23/5/2480 |
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