Classification of EEG signals in an object recognition task

Rus, Iacob D.; Marc, Paul; Dînşoreanu, Mihaela; Potolea, Rodica; Mureșan, Raul C.

doi:10.1109/iccp.2017.8117036

Cited by 13 publications

(15 citation statements)

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“…The average classification accuracy over all three object exploration modalities was 83.89% [14]. Rus et al found that the EEG features in gamma wave were more suitable for object recognition, and they used three different classifiers (SVM, KNN, and ANN) to classify objects, yielding accuracies of 89.5%, 89.5%, and 83%, respectively [15], consistent with the highest classification accuracy obtained using gamma waves in all brain waves in this study. Using EEG signals, Lam et al implemented a single-layer neural network method to identify and classify landscape images and animal images, yielding an average accuracy of 91.15%, in which the average recognition rate of landscape images was 89.69%, and that of animal images was 92.34% [17].…”

Section: Recognition Accuracy Of Landscape Structure and Color Based On Machine Learningsupporting

confidence: 86%

“…Fast Fourier transform was used to extract frequency band information. T frequency-domain features were extracted to obtain the logarithmic frequency energy v ues of the waves in five frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 H beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (>30 Hz) [43]. The absolute EEG value for various types of pictures show that the natural landsca play a certain role in recovery from stress, whereby alpha waves (8-13 Hz) and beta wav (13-30 Hz) are the most suitable indicators [3].…”

Section: Methodsmentioning

confidence: 99%

“…EEG features (frequency-domain features, time-domain features, and spatial-domain features) in EEG signals represent the brain region activities. Thus far, there have been many studies using EEG technology and machine learning for recognition with good classification accuracy, e.g., emotion recognition [10][11][12][13], object structure recognition [14,15], color recognition [16], and landscape and animal image recognition [17]. Some studies have gradually applied EEG technology to different fields, including environmental perception and landscape assessment [6,[18][19][20], while others have also explored the impact of specific environmental characteristics on the natural environment [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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The Function of Color and Structure Based on EEG Features in Landscape Recognition

Wang

2021

IJERPH

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Both color and structure make important contributions to human visual perception, as well as the evaluation of landscape quality and landscape aesthetics. The EEG equipment liveamp32 was used to record the EEG signals of humans when viewing landscape images, structure images with filtered color, and color images with a filtered structure. The results show that the SVM classifier was the most suitable classifier for landscape classification based on EEG features. The classification accuracy of the landscape picture recognition was up to 98.3% when using beta waves, while the accuracy of the color recognition was 97.5%, and that of the structure recognition was 93.9% when using gamma waves. Secondly, color and structure played a major role in determining the alpha and gamma wave responses, respectively, for all the landscape types, including forest, desert, and water. Furthermore, structure only played a decisive role in forest, while color played a major role in desert and water when using beta waves. Lastly, statistically significant differences between landscape groups and scenario groups with regard to alpha, beta, and gamma rhythms in brain waves were confirmed. The reasonable usage and layout of structure and color will have a very important guiding value for landscape aesthetics in future landscape design and landscape planning.

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Section: Recognition Accuracy Of Landscape Structure and Color Based On Machine Learningsupporting

confidence: 86%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Function of Color and Structure Based on EEG Features in Landscape Recognition

Wang

2021

IJERPH

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“…Our investigation revealed that FFT, PCA, WT, and AR were the most widely used and effective methods for feature extraction among the reviewed articles in this domain; these methods were reported as superior 31%, 19%, 15%, and 15% of the time, respectively. For example, FFT has been implemented by researchers in several published studies [ 81 , 196 , 211 , 239 , 240 , 241 , 242 ] to extract features on the basis of the frequency of the EEG signals; however, another study has confirmed AR as one of the most reliable methods [ 243 ]. We also identified several essential feature extraction techniques that were less frequently applied in MWL tasks, including entropy and HHT, which elicited features of both nonlinear and non-stationary signals.…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, Shah and Ghosh [ 55 ] have developed a real-time classification system by using PCA and a simple KNN classification algorithm. Interestingly, the studies in references [ 196 , 241 ] have proposed using FFT to evaluate the PSD on the basis of the time domain features incorporated in different ML models for classification. The results indicated the highest accuracy with KNN, at 99.42% and 90.5%, respectively.…”

Section: Discussionmentioning

confidence: 99%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

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A design of bat-based optimized deep learning model for EEG signal analysis

Gupta¹,

Kanungo²,

Kumar

et al. 2023

Multimed Tools Appl

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Depression is one of the mental illnesses that negatively affect a person's thinking, action, and feeling. Thus the rate of depression is identified by analyzing Electroencephalogram (EEG) signals, but it has the problem of classifying depression rate because of noise. In this paper, a novel Bat-based UNET Signal Analysis (BUSA) framework is designed to organize the depression rate of patients with an EEG dataset. This technique involves preprocessing, feature selection, feature extraction, and classification. After the data training process preprocessing function was activated to remove the noise in the brain signal. Hereafter, the noiseless signal is used for the further process.Here, the fitness of the bat is upgraded in the UNET classification layer. Moreover, the brain signal's feature selection and depression rate were classified using the bat fitness that has helped to gain the desired output. Finally, performance metrics of the proposed BUSA technique are compared with other existing methods regarding the accuracy, AUC, precision, recall, and power. In that, the developed framework has attained better results to classify depression rates.

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Classification of EEG signals in an object recognition task

Cited by 13 publications

References 1 publication

The Function of Color and Structure Based on EEG Features in Landscape Recognition

The Function of Color and Structure Based on EEG Features in Landscape Recognition

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

A design of bat-based optimized deep learning model for EEG signal analysis

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