Game induced emotion analysis using electroencephalography

Khan, Amna Nauman; Rasool, Shahzad

doi:10.1016/j.compbiomed.2022.105441

Cited by 18 publications

(9 citation statements)

References 50 publications

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“…The performance of the proposed approach is superior compared to the existing methods.The system for recognizing consumer choice can be adversely affected by an increase in sensitivity with low specificity. The accuracy is 88%, but specificity is 86.76%, which reveals the high false alarm rate that affects the recognition of consumer choice negatively [13]. It is possible to detect the consumer preference on various e-commerce products; however, Jafar et al [65] were unable to obtain sensitivity and specificity greater than 69%.…”

Section: Evaluation Of the Effectiveness Of The Proposed Methods In C...mentioning

confidence: 97%

“…In any kind of machine learning application, data is usually in raw form and needs some sort of preprocessing before it is usable for feature extraction. For many decisionmaking sectors, the automatic analysis of diverse and multimodal data and the instantaneous extraction of information by using machine learning approaches have become major challenges [11][12][13][14].…”

Section: Preprocessing Of Eeg Signalsmentioning

confidence: 99%

“…For preprocessing, several techniques have been discussed here. Amna et al [13] removed the noise from EEG signals by using independent component analysis (ICA). Abeer et al [2] used bandpass filter for noise removal.…”

Section: Preprocessing Of Eeg Signalsmentioning

confidence: 99%

“…The beta band (14-30 Hz) represents the occupied or busy brain. The alpha band (8)(9)(10)(11)(12)(13)(14) shows the calmness of the brain. The theta band (4-8 Hz) reflects the excitement.…”

Section: Introductionmentioning

confidence: 99%

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An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Shah

Usman

Khalid

et al. 2022

Sensors

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Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

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Section: Evaluation Of the Effectiveness Of The Proposed Methods In C...mentioning

confidence: 97%

Section: Preprocessing Of Eeg Signalsmentioning

confidence: 99%

Section: Preprocessing Of Eeg Signalsmentioning

confidence: 99%

“…The beta band (14-30 Hz) represents the occupied or busy brain. The alpha band (8)(9)(10)(11)(12)(13)(14) shows the calmness of the brain. The theta band (4-8 Hz) reflects the excitement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Shah

Usman

Khalid

et al. 2022

Sensors

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show abstract

“…These tasks involve a variety of noise stimuli delivered through headphones while being performed in different environments. In [108] four basic emotional states were assessed: happiness, relaxation, boredom, and stress. Information was acquired through random convenience sampling, which involved recruiting 31 participants of various ages.…”

Section: ) Visual Taskmentioning

confidence: 99%

Study of the Acute Stress Effects on Decision Making Using Electroencephalography and Functional Near-Infrared Spectroscopy: A Systematic Review

Abdalhadi,

Koundal,

Yusoff

et al. 2024

IEEE Access

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This systematic review provides a comprehensive analysis of studies that use electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to investigate how acute stress affects decision-making processes. The primary goal of this systematic review was to examine the influence of acute stress on decision making in challenging or stressful situations. Furthermore, we aimed to identify the specific brain regions affected by acute stress and explore the feature extraction and classification methods employed to enhance the detection of decision making under pressure. Five academic databases were carefully searched and 27 papers that satisfied the inclusion criteria were found. Overall, the results indicate the potential utility of EEG and fNIRS as techniques for identifying acute stress during decisionmaking and for gaining knowledge about the brain mechanisms underlying stress reactions. However, the varied methods employed in these studies and the small sample sizes highlight the need for additional studies to develop more standardized approaches for acute stress effects in decision-making tasks. The implications of the findings for the development of stress induction and technology in the decision-making process are also explained.

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Multi-domain Feature Extraction Methods for Classification of Human Emotions from Electroencephalography (EEG) Signals

Kapagate,

Bethany,

Jain

et al. 2024

Communications in Computer and Information Science

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Game induced emotion analysis using electroencephalography

Cited by 18 publications

References 50 publications

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Study of the Acute Stress Effects on Decision Making Using Electroencephalography and Functional Near-Infrared Spectroscopy: A Systematic Review

Multi-domain Feature Extraction Methods for Classification of Human Emotions from Electroencephalography (EEG) Signals

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