Odor Pleasantness Classification from Electroencephalographic Signals and Emotional States

Becerra, Miguel A.; Londoño-Delgado, Edwin; Pelaez-Becerra, S. M.; Serna-Guarín, Leonardo; Castro-Ospina, Andrés Eduardo; Marín-Castrillón, Diana M.; Peluffo-Ordóñez, Diego H.

doi:10.1007/978-3-319-98998-3_10

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…Consequently, we recommend conducting in-depth studies that include other fields, such as studies on cognitive load [155] and multisensorial stimuli [156,157]. This would serve to take advantage of all human senses, considering that the presentation of information affects users' perception, which may be very relevant for fast decision-making.…”

Section: Discussionmentioning

confidence: 99%

Information Quality Assessment for Data Fusion Systems

Becerra

Tobón

Castro-Ospina

et al. 2021

Data

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This paper provides a comprehensive description of the current literature on data fusion, with an emphasis on Information Quality (IQ) and performance evaluation. This literature review highlights recent studies that reveal existing gaps, the need to find a synergy between data fusion and IQ, several research issues, and the challenges and pitfalls in this field. First, the main models, frameworks, architectures, algorithms, solutions, problems, and requirements are analyzed. Second, a general data fusion engineering process is presented to show how complex it is to design a framework for a specific application. Third, an IQ approach, as well as the different methodologies and frameworks used to assess IQ in information systems are addressed; in addition, data fusion systems are presented along with their related criteria. Furthermore, information on the context in data fusion systems and its IQ assessment are discussed. Subsequently, the issue of data fusion systems’ performance is reviewed. Finally, some key aspects and concluding remarks are outlined, and some future lines of work are gathered.

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Section: Discussionmentioning

confidence: 99%

Information Quality Assessment for Data Fusion Systems

Becerra

Tobón

Castro-Ospina

et al. 2021

Data

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“…In this work, a frequency band generator is designed to extract the frequency band combination S based on the PSD features F. Since different frequency bands of the EEG signal may contain different information, our goal is to extract as many useful frequency bands as possible to form a frequency band combination. Inspired by the traditional EEG frequency spectrum division method based on biological rhythm involving five sub-bands [24] and the recent average frequency band division method in which the PSD features are divided into several sub-bands with the same frequency bandwidth [20], we select five window lengths of frequency band slice: 1, 5, 10, 15, and 20 Hz, i.e., L i ∈ {1, 5, 10, 15, 20}. The increment between two neighboring frequency band slices G is consistently 1 Hz.…”

Section: B Frequency Band Generatormentioning

confidence: 99%

An Olfactory EEG Signal Classification Network Based on Frequency Band Feature Extraction

Sun¹,

Wei²,

Peng³

et al. 2022

Preprint

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Classification of olfactory-induced electroencephalogram (EEG) signals has shown great potential in many fields. Since different frequency bands within the EEG signals contain different information, extracting specific frequency bands for classification performance is important. Moreover, due to the large inter-subject variability of the EEG signals, extracting frequency bands with subject-specific information rather than general information is crucial. Considering these, the focus of this letter is to classify the olfactory EEG signals by exploiting the spectral-domain information of specific frequency bands. In this letter, we present an olfactory EEG signal classification network based on frequency band feature extraction. A frequency band generator is first designed to extract frequency bands via the sliding window technique. Then, a frequency band attention mechanism is proposed to optimize frequency bands for a specific subject adaptively. Last, a convolutional neural network (CNN) is constructed to extract the spatio-spectral information and predict the EEG category. Comparison experiment results reveal that the proposed method outperforms a series of baseline methods in terms of both classification quality and inter-subject robustness. Ablation experiment results demonstrate the effectiveness of each component of the proposed method.

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“…In literature, various stimuli have been used to evoke human emotions that engage either a single human sense [ 37 , 38 , 39 , 40 , 41 ] or two human senses [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. These evoked emotions are then recognized using features extracted from data acquired using different physiological sensors.…”

Section: Related Workmentioning

confidence: 99%

“…An asymmetry index method was introduced for EEG based emotion recognition in response to images [ 51 ]. Different odors were used to recognize emotions using content that engaged the sense of olfaction [ 39 ]. EEG signals were used to analyze different brain regions to discriminate pleasant and unpleasant odors.…”

Section: Related Workmentioning

confidence: 99%

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia

Raheel

Majid

Alnowami

et al. 2020

Sensors

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Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57 % as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76 % (for four emotions) when interacting with tactile enhanced multimedia.

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Odor Pleasantness Classification from Electroencephalographic Signals and Emotional States

Cited by 14 publications

References 22 publications

Information Quality Assessment for Data Fusion Systems

Information Quality Assessment for Data Fusion Systems

An Olfactory EEG Signal Classification Network Based on Frequency Band Feature Extraction

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia

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