Database for an emotion recognition system based on EEG signals and various computer games – GAMEEMO

Alakuş, Talha Burak; Gönen, Murat; Türkoğlu, İ̇brahim

doi:10.1016/j.bspc.2020.101951

Cited by 103 publications

(54 citation statements)

References 29 publications

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“…These results are consistent, but not as good, with those obtained for single electrodes by the authors of the data (Alakus et al, 2020), as verified in a private email with the lead author. However, here the model includes quantum effects from tripartite influences.…”

Section: Resultssupporting

confidence: 85%

“…The authors of the data used multiple trial-and-error parameters in their neural-network calculations, as well as the use of several classification filters and kernels (Alakus et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…The EEG data used for this study was made publicly available via Mendeley from a study of emotion recognition on 4 types of computer games (Alakus et al, 2020). The computer games consist of 4 types of affective behavior:…”

Section: Data Sources: Gameemomentioning

confidence: 99%

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Quantum Calcium-Ion Affective Influences Measured by EEG

Ingber¹

2020

Preprint

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Background: Since circa 1980, a model of neocortical interactions, Statistical Mechanics of Neocortical Interactions (SMNI) has been successful in calculating many experimental phenomena, including fits to electroencephalographic (EEG) data in attention tasks, using an importance-sampling code Adaptive Simulated Annealing (ASA). The SMNI model is developed in the context of classical path-integrals, which affords intuitive insights as well as direct numerical benefits, e.g., using the effective Action as a a cost/objective function for parameter fits to data. Objective: Previous authors have fit affective EEG data to neural-network models. This project seeks to use models based on physics and biology to fit this same data. Previous work showed improvements in fits to EEG for attention states; this project extends these methods to affective states. Method: Path integrals are used in both classical and quantum contexts. Classical path integrals are used to define a cost/objective function to fit data, and quantum path integrals are used to derive a closed-form analytic expression for Ca-ion waves in the presence of a magnetic vector potential which is generated by highly synchronous neuronal firings which give rise to EEG. ASA is used to fit EEG data. Results: The mathematical-physics and computer parts of the study are successful, in that cost/objective functions used to fit EEG data using these models are consistent with previous work published by other authors. However, since the SMNI model includes these quantum effects, this is another reason to continue examining these issues. The results here are consistent, not better, than previous work using neural-network models, albeit only one parameter was used here, instead of multiple filters and kernels used previously on such data. Conclusion: Although these quantum effects are highly speculative, explicit calculations have shown them to be consistent with experimental data, at least to date. The current supercomputer project extends this model to affective/emotion data. Results from several authors using neural-network approaches at individual electrode sites show some predictive capabilities; the results given here are consistent with these other results. However, since the SMNI model includes these quantum effects, this is another reason to continue examining these issues.

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Section: Resultssupporting

confidence: 85%

Section: Resultsmentioning

confidence: 99%

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Quantum Calcium-Ion Affective Influences Measured by EEG

Ingber¹

2020

Preprint

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“…Sample length of each EEG data is 38,252. More technical and detailed information about the data set can be obtained from [14 ]. Researchers who want to use GAMEEMO data can access the data from the link provided (https://data.mendeley.com/datasets/b3pn4kwpmn/3 ).…”

Section: Methodsmentioning

confidence: 99%

Emotion recognition with deep learning using GAMEEMO data set

Alakuş

Türkoğlu

2020

Electron. lett.

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Emotion recognition is actively used in brain–computer interface, health care, security, e‐commerce, education and entertainment applications to increase and control human–machine interaction. Therefore, emotions affect people's lives and decision‐making mechanisms throughout their lives. However, the fact that emotions vary from person to person, being an abstract concept and being dependent on internal and external factors makes the studies in this field difficult. In recent years, studies based on electroencephalography (EEG) signals, which perform emotion analysis in a more robust and reliable way, have gained momentum. In this article, emotion analysis based on EEG signals was performed to predict positive and negative emotions. The study consists of four parts. In the first part, EEG signals were obtained from the GAMEEMO data set. In the second stage, the spectral entropy values of the EEG signals of all channels were calculated and these values were classified by the bidirectional long‐short term memory architecture in the third stage. In the last stage, the performance of the deep‐learning architecture was evaluated with accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curve. With the proposed method, an accuracy of 76.91% and a ROC value of 90% were obtained.

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“…All the data are annotated properly in terms of valence, arousal, and dominance of people. In [ 25 ], Alakus et al offered an EEG database called GAMEEMO for various games.…”

Section: Introductionmentioning

confidence: 99%

An EEG Database and Its Initial Benchmark Emotion Classification Performance

Seal

Reddy

Chaithanya

et al. 2020

Computational and Mathematical Methods in Medicine

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Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.

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Database for an emotion recognition system based on EEG signals and various computer games – GAMEEMO

Cited by 103 publications

References 29 publications

Quantum Calcium-Ion Affective Influences Measured by EEG

Quantum Calcium-Ion Affective Influences Measured by EEG

Emotion recognition with deep learning using GAMEEMO data set

An EEG Database and Its Initial Benchmark Emotion Classification Performance

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