Computing stress-related emotional state via frontal cortex asymmetry to be applied in passive-ssBCI

Cotrina, Anibal; Garcia, Javier C.; Benevides, Alessandro B.; Longo, Berthil; Ferreira, André; Porner-Escher, Alexandre; Souza, Maria Dolores Pinheiro de; Bastos, Teodiano

doi:10.1109/brc.2014.6880974

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…Several studies have demonstrated the correlations between EEG pattern and emotional states, i.e., calmness, depression, excitement [9][10][11]. In [10], an EEG-based analysis on the frontal alpha asymmetry index with a support vector machine (SVM) algorithm was proposed for stress analysis. In [11], a frequency domain-based analysis with a k-nearest neighbor (k-NN) algorithm was proposed to analyze the stress state using the EEG signal.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

Hasan

Kim

2019

Brain Sciences

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“…Wheeler et al [ 6 ] observed a negative emotional state when the right hemisphere activity of the cerebral cortex is active during the stress period, and several studies showed that researchers investigate a correlation between EEG measurements and depression. Based on these studies, Atencio et al [ 7 ] used the frontal alpha asymmetry index to obtain the emotional stress state using the EEG public database and classified them using feature extraction and SVM. Bastos-Filho et al [ 8 ] extracted signal features to detect stress based on EEG signals and classified them using k -NN.…”

Section: Introductionmentioning

confidence: 99%

“…Bastos-Filho et al [ 8 ] extracted signal features to detect stress based on EEG signals and classified them using k -NN. However, in [ 7 , 8 ], a large number of features were used to classify, which generally causes a problem called the curse of dimensionality. With many features, the size of the learning set required for modeling must be proportionally large, which takes a long time to categorize.…”

Section: Introductionmentioning

confidence: 99%

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

Shon

Park

et al. 2018

IJERPH

102

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In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and k-nearest neighbor (k-NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals. GA is incorporated in the stress analysis pipeline to effectively select subset of features that are suitable to enhance the performance of the k-NN classifier. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP), which is a public EEG dataset. A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. The selected features through GA are used as input to the k-NN classifier to distinguish whether each EEG datapoint represents a stress state. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle component analysis (PCA) method. Experimental results show that the proposed GA-based method outperforms PCA, with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA. Thus, it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.

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“…Since asymmetry index is the output of a passive-BCI, it can scale by multiplying parameters such as the closer peak response frequency f h (See equation 2) and window length n (See Section III-A) of a reactive-BCI to maintain the success rate. Regarding the computation of the alpha band asymmetry, our preliminary results shown in [23] indicates that asymmetry in the frontal lobe is significantly associated with human emotion reactivity [12]. The next step in this research will be to compute the asymmetry index and to propose a linear equation that relates this index with SSVEPbased BCI parameters.…”

Section: Discussionmentioning

confidence: 94%

Towards an architecture of a hybrid BCI based on SSVEP-BCI and passive-BCI

Cotrina

Benevides

Ferreira

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Recent decades have seen BCI applications as a novel and promising new channel of communication, control and entertainment for disabled and healthy people. However, BCI technology can be prone to errors due to the basic emotional state of the user: the performance of reactive and active BCIs decrease when user becomes stressed or bored, for example. Passive-BCI is a recent approach that fuses BCI technology with cognitive monitoring, providing valuable information about the user's intentions, the situational interpretations and mainly the emotional state. In this work, an architecture composed by passive-BCI co-working with SSVEP-BCI is proposed, with the aim of improving the performance of the reactive-BCI. The possibility of adjusting recognition characteristics of SSVEP-BCIs using a passive-BCI output is evaluated. In this sense, two ways to recover the accuracy of SSVEP are presented in this paper: 1) Adjusting of Amplitude of the SSVEP and 2) Adjusting of Frequency of the SSVEP response. The results are promising, because accuracy of SSVEP-BCI can be recovered in the case that it was reduced by the BCI user's emotional state.

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Computing stress-related emotional state via frontal cortex asymmetry to be applied in passive-ssBCI

Cited by 6 publications

References 17 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

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

Towards an architecture of a hybrid BCI based on SSVEP-BCI and passive-BCI

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