Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

Shon, Dongkoo; Im, Ki Chun; Park, Jeong-Ho; Lim, Dong-Sun; Jang, Byungtae; Kim, Jong‐Myon

doi:10.3390/ijerph15112461

Cited by 112 publications

(86 citation statements)

References 26 publications

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“…However, when a participant provides the self-assessment rating for the same video, from the participant rating list (available on [15]), it can be observed that the video from the genre of calmness brings the feeling of stress and vice-versa. Therefore, for this research, the online self-assessment rating is considered to categorize the experiment IDs (either calm or stress) for each participant by Equations (1) and (2), derived from [11,20,21].…”

Section: Dataset Description and Annotationmentioning

confidence: 99%

“…From the time domain, the extracted statistical features are root mean square (F1), square mean root (F2), peak to peak (F3), kurtosis (F4), skewness (F5), kurtosis factor (F6), shape factor (F7), crest factor (F8), and impulse factor (F9). In addition to these statistical feature parameters, Hjorth parameters are also considered to compute the mobility and complexity of the signal [20,23,24]. These two parameters contain the information on the frequency spectrum of the signal.…”

Section: Statistical Features From the Time Domainmentioning

confidence: 99%

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A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals

Hasan

Kim

2019

Brain Sciences

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Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain-specific information pool to develop an effective machine learning model. In this study, a multi-domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet-based bandwidth specific feature analysis from the time-frequency domain. Then, a wrapper-based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the non-redundant features. Finally, the k-nearest neighbor (k-NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non-linear dimensionality reduction techniques, as well as those without feature ranking.

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Section: Dataset Description and Annotationmentioning

confidence: 99%

Section: Statistical Features From the Time Domainmentioning

confidence: 99%

A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals

Hasan

Kim

2019

Brain Sciences

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“…The system achieved an 86.12% accuracy on average. In [ 80 ], emotional stress state detection using a genetic algorithm and k-NN based on EEG signals is proposed. It achieved a 71.76% accuracy.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Becerra-Sánchez

Reyes-Muñoz

Ibáñez

2020

Sensors

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In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.

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“…Subsequent to presentation of visual stimuli to a subject, multi-channel signals of EEG are recorded, and afterwards, the signal labeling is done based on subject ratings. The previous work on EEG emotional features extraction [25][26][27][28][29] has revealed that there exist several valuable features of time, frequency, and time-frequency that have been evidenced to be efficient in differentiating emotions. Furthermore, there is no standard feature set that has been agreed as the most appropriate for EEG emotion classification.…”

Section: Literature Review On Eeg-based Emotion Recognitionmentioning

confidence: 99%

“…Some of these approaches are demonstrated in Table 2. Few works have presented hybrid methods of evolutionary computer algorithms and classification methods [25], where the objective of these works is to deal with the high dimensionality issue of EEG emotion recognition. Therefore, in this work, we propose a novel fuzzy c-means-genetic algorithm-neural network (FCM-GA-NN) model for EEG emotion recognition.…”

Section: Literature Review On Eeg-based Emotion Recognitionmentioning

confidence: 99%

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

2019

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In multi-modal emotion aware frameworks, it is essential to estimate the emotional features then fuse them to different degrees. This basically follows either a feature-level or decision-level strategy. In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. To overcome issues of feature extraction and multi-modal fusion, hybrid fuzzy-evolutionary computation methodologies are employed to demonstrate ultra-strong capability of learning features and dimensionality reduction. This paper proposes a novel multi-modal emotion aware system by fusing speech with EEG modalities. Firstly, a mixing feature set of speaker-dependent and independent characteristics is estimated from speech signal. Further, EEG is utilized as inner channel complementing speech for more authoritative recognition, by extracting multiple features belonging to time, frequency, and time–frequency. For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. To fuse speech with EEG information, a separate classifier is used for each modality, then output is computed by integrating their posterior probabilities. Results show the superiority of the proposed model, where the overall performance in terms of accuracy average rates is 98.06%, and 97.28%, and 98.53% for EEG, speech, and multi-modal recognition, respectively. The proposed model is also applied to two public databases for speech and EEG, namely: SAVEE and MAHNOB, which achieve accuracies of 98.21% and 98.26%, respectively.

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Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

Cited by 112 publications

References 26 publications

A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals

A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

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