Quick breast cancer detection via classification of evoked EEG potentials in the mammologist's brain

Sulimova, Valentina; Windridge, David; Bukhonov, Sergey; Mottl, Vadim; Krasotkina, Olga

doi:10.1109/sibircon48586.2019.8958286

Cited by 3 publications

(1 citation statement)

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“…Despite the vast amount of data that can be gleaned from an EEG reading, little research has been done on using it to identify mental stress. Furthermore, the research that has already been done does not consistently point to a general strategy for methodically combining machine learning with EEG for stress assessment [7]. This was demonstrated when various machine learning classifiers and feature extraction techniques were used in comparable circumstances.…”

Section: Introductionmentioning

confidence: 98%

EEG Feature Extraction based on Fast Fourier Transform and Wavelet Analysis for Classification of Mental Stress Levels using Machine Learning

Kit,

Amin,

et al. 2023

Adv. sci. technol. eng. syst. j.

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Mental stress assessment remains riddled with biases caused by subjective reports and individual differences across societal backgrounds. To objectively determine the presence or absence of mental stress, there is a need to move away from the traditional subjective methods of self-report questionnaires and interviews. Previously, it has been evidence that EEG Oscillations can discriminate mental states, for instance, stressed and non-stressed. However, it is still not clear in which range of EEG oscillations the neural activities are associated with the mental states. This paper presents a wavelet-based EEG feature extraction method for the classification of mental stress using machine learning classifiers. An EEG dataset of 22 participants was used to test the performance of the proposed waveletbased feature extraction method. The dataset includes both stress and control conditions, and the stress condition has multiple levels of stress, starting from low, mild, and high stress. The Daubechies mother wavelet of the fourth order was used to separate the EEG oscillations into 7 levels for the extraction of the absolute powers. Whereas Fast Fourier Transform were implemented to obtain the average power of the oscillations. The features were then used in support vector machine, decision tree, linear discriminant analysis and artificial neural network classifiers. A comparison between the classifiers using average power, absolute power, and a combination of both is provided. The EEG alpha, theta, and beta frequency bands showed promising results for the classification of mental stress vs. control conditions by achieving an average accuracy of 95% using the decision tree. The results of the proposed method suggest the potential use of wavelet analysis for mental stress detection despite FFT performing better. The proposed method has the potential to be used in Computer-Aided Diagnosis (CAD) systems for mental stress assessment in the future alongside the discovery of significant wave bands in relation to mental stress detection.

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Section: Introductionmentioning

confidence: 98%