Emotion Detection Through EEG Signals Using FFT and Machine Learning Techniques

Saxena, Anvita; Tripathi, K. C.; Khanna, Ashish; Gupta, Deepak; Sundaram, Shirsh

doi:10.1007/978-981-15-1286-5_46

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…At data analysis and feature extraction stages, FFT, which is described in Eqn. 9, is applied in the frequency domain to extract the mean power spectrum for EEG main bands, delta (0.1-3.9 Hz), theta (4-7.9 Hz), alpha (8-13.9 Hz), beta (14-29.9 Hz), and gamma (30-80 Hz) for 16 channels covering the frontal lobe [61][62][63].…”

Section: Analysis Of the Frontal Lobe Eeg Signalmentioning

confidence: 99%

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

AlShorman¹,

Masadeh²,

Heyat³

et al. 2022

J. Integr. Neurosci.

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Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical e fects. Moreover, hormones, physiological e fects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones' instability. Therefore, it is crucial to study stress using di ferent mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is applied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands' power density for the frontal lobe. A ter that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications.

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Section: Analysis Of the Frontal Lobe Eeg Signalmentioning

confidence: 99%

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

AlShorman¹,

Masadeh²,

Heyat³

et al. 2022

J. Integr. Neurosci.

View full text Add to dashboard Cite

show abstract

“…The Fast Fourier Transform (FFT) is an algorithm that decomposes a complex signal into smaller transformations, using a discrete Fourier transform (DFT) for a sequence and an inverse (IDFT) [32,43,44]. FFT is one of the most used algorithms in signal analysis [45].…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

“…As for the features that are extracted based on FFT, they depend on decomposing the original signal into smaller transformations. Then, to obtain the resulting signal, the decaying signals are combined with the decaying signals and removing the low-frequency signals [43][44][45].…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues

Abdulrahman¹,

Baykara²

2021

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Emotion classification based on physiological signals has become a hot topic in the past decade. Many studies have attempted to classify emotions using various techniques, to discover human emotions accurately. This study focused on listing the most recent studies that have classified emotions based on electroencephalogram (EEG) signals. This study also focused on solving the problems and challenges facing researchers in emotion classification and EEG applications used in several fields. The plan of this study is based on a strategy with three aspects within specific rules: The first aspect is the methods; we chose studies that included new methods to extract features. The second aspect is the data sets. We tried to choose a study that classified the same data set. The third aspect is applications; we have listed many applications of the EEG in several areas. We concluded from this study that detecting human emotions using the EEG signals is one of the most reliable and widely used methods of detecting emotions in the past few years. Also, we have noticed that the EEG can detect human emotions, especially in psychiatry, for example, for epileptic patients whose emotions cannot be extracted using traditional methods such as facial expressions and tone of voice.

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“…Abdelrahaman et al [1] also achieved 80% accuracy by using k-NN for their offline SVS. k-NN has also been applied in related fields such as text recognition [29], iris recognition [30], and emotion detection [25]. This work is an extension of the performance evaluation of the k-NN [24] work and presents a novel optimized k-NN online signature-verification system.…”

Section: Related Workmentioning

confidence: 99%

Optimized jk-nearest neighbor based online signature verification and evaluation of the main parameters

Saleem

Kővári

2021

csci

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In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) classifier for online signature verification. After studying the algorithm's main parameters, we use four separate databases to present and evaluate each algorithm parameter. The results show that the proposed method can increase the verification accuracy by 0.73-10% compared to a traditional one class k-NN classifier. The algorithm has achieved reasonable accuracy for different databases, a 3.93% error rate when using the SVC2004 database, 2.6% for MCYT-100 database, 1.75% for the SigComp'11 database, and 6% for the SigComp'15 database.The proposed algorithm uses specifically chosen parameters and a procedure to pick the optimal value for K using only the signer's reference signatures, to build a practical verification system for real-life scenarios where only these signatures are available. By applying the proposed algorithm, the average error achieved was 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp'15, and 2.22% for SigComp'11.

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Emotion Detection Through EEG Signals Using FFT and Machine Learning Techniques

Cited by 11 publications

References 14 publications

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues

Optimized jk-nearest neighbor based online signature verification and evaluation of the main parameters

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