An EEG Database and Its Initial Benchmark Emotion Classification Performance

Seal, Ayan; Reddy, Puthi Prem Nivesh; Chaithanya, Pingali; Meghana, Arramada; Jahnavi, Kamireddy; Krejcar, Ondřej; Hudák, Radovan

doi:10.1155/2020/8303465

Cited by 29 publications

(11 citation statements)

References 33 publications

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“…In the research conducted so far, a few databases with physiological data dedicated to emotion recognition experiments have been published, including DEAP, MAHNOB-HCI [16], DREAMER [17], HR-EEG4EMO [18], MPED [19] or Seal et al's EEG database [20]. However, they are all intended only to store data from experiments conducted by the authors.…”

Section: Related Workmentioning

confidence: 99%

Graph Representation Integrating Signals for Emotion Recognition and Analysis

Zawadzka

Wierciński

Meller

et al. 2021

Sensors

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Data reusability is an important feature of current research, just in every field of science. Modern research in Affective Computing, often rely on datasets containing experiments-originated data such as biosignals, video clips, or images. Moreover, conducting experiments with a vast number of participants to build datasets for Affective Computing research is time-consuming and expensive. Therefore, it is extremely important to provide solutions allowing one to (re)use data from a variety of sources, which usually demands data integration. This paper presents the Graph Representation Integrating Signals for Emotion Recognition and Analysis (GRISERA) framework, which provides a persistent model for storing integrated signals and methods for its creation. To the best of our knowledge, this is the first approach in Affective Computing field that addresses the problem of integrating data from multiple experiments, storing it in a consistent way, and providing query patterns for data retrieval. The proposed framework is based on the standardized graph model, which is known to be highly suitable for signal processing purposes. The validation proved that data from the well-known AMIGOS dataset can be stored in the GRISERA framework and later retrieved for training deep learning models. Furthermore, the second case study proved that it is possible to integrate signals from multiple sources (AMIGOS, ASCERTAIN, and DEAP) into GRISERA and retrieve them for further statistical analysis.

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Section: Related Workmentioning

confidence: 99%

Graph Representation Integrating Signals for Emotion Recognition and Analysis

Zawadzka

Wierciński

Meller

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Alpha and Beta waves are small sharp waves that are often observed when subjects are induced with interaction, which is the opposite of a relaxed state (Seal et al, 2020). Frequencies of brainwaves are divided into five distinct categories with one additional category for spike potential, as described in Table 2.…”

Section: Technical Aspects Of Eeg Systemmentioning

confidence: 99%

“…Technically, when the body is in deep rest. These brainwaves are vital for the rejuvenation of the body and brain (Seal et al, 2020).…”

Section: Technical Aspects Of Eeg Systemmentioning

confidence: 99%

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Data Acquisition and Data Processing using Electroencephalogram in Neuromarketing: A Review

Amran¹,

Ibrahim²,

Malim³

et al. 2021

JST

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Electroencephalogram (EEG) is a neurotechnology used to measure brain activity via brain impulses. Throughout the years, EEG has contributed tremendously to data-driven research models (e.g., Generalised Linear Models, Bayesian Generative Models, and Latent Space Models) in Neuroscience Technology and Neuroinformatic. Due to versatility, portability, cost feasibility, and non-invasiveness. It contributed to various Neuroscientific data that led to advancement in medical, education, management, and even the marketing field. In the past years, the extensive uses of EEG have been inclined towards medical healthcare studies such as in disease detection and as an intervention in mental disorders, but not fully explored for uses in neuromarketing. Hence, this study construes the data acquisition technique in neuroscience studies using electroencephalogram and outlines the trend of revolution of this technique in aspects of its technology and databases by focusing on neuromarketing uses.

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“…While several studies have been conducted on emotion detection based on facial expressions, its reliability has been questioned. The fact that facial expressions can be imitated, meaning that the real emotion felt on the inside is conveyed or silenced differently with gestures and facial expressions, has harmed the studies' accuracy [5]. This has conducted emotion detection toward physiological signals like EEG and electrocardiogram (ECG) [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Wavelet ELM-AE Based Data Augmentation and Deep Learning for Efficient Emotion Recognition Using EEG Recordings

et al. 2022

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Emotion perception is critical for behavior prediction. There are many ways to capture emotional states by observing the body and copying actions. Physiological markers such as electroencephalography (EEG) have gained popularity, as facial emotions may not always adequately convey true emotion. This study has two main aims. The first is to measure four emotion categories using deep learning architectures and EEG data. The second purpose is to increase the number of samples in the dataset. To this end, a novel data augmentation approach namely the Extreme Learning Machine Wavelet Auto Encoder (ELM-W-AE) is proposed for data augmentation. The proposed data augmentation approach is both simple and faster than the other synthetic data augmentation approaches. For deep architectures, large datasets are important for performance. For this reason, data multiplexing approaches with classical and synthetic methods have become popular recently. The proposed synthetic data augmentation is the ELM-W-AE because of its efficiency and detail reproduction. The ELM-AE structure uses wavelet activation functions such as Gaussian, GgW, Mexican, Meyer, Morlet, and Shannon. Deep convolutional architectures classify EEG signals as images. EEG waves are scalogram using Continuous Wavelet Transform (CWT). The ResNet18 architecture recognizes emotions. The proposed technique uses GAMEMO data collected during gameplay. Each of these states is represented in the GAMEMO data collection. The visual data set created from the signal was divided into two groups as 70% training and 30% testing. ResNet18 has been fine-tuned with augmented photos, training images only. It achieved 99.6% classification accuracy in tests. The proposed method is compared with the other approaches on the same dataset, and an approximately 22% performance improvement is achieved.

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An EEG Database and Its Initial Benchmark Emotion Classification Performance

Cited by 29 publications

References 33 publications

Graph Representation Integrating Signals for Emotion Recognition and Analysis

Graph Representation Integrating Signals for Emotion Recognition and Analysis

Data Acquisition and Data Processing using Electroencephalogram in Neuromarketing: A Review

Wavelet ELM-AE Based Data Augmentation and Deep Learning for Efficient Emotion Recognition Using EEG Recordings

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